NetMob 2011
Workshop on the Analysis of
Mobile Phone Networks
MIT (Cambridge, USA)
October 10-11, 2011

Book of abstracts

Editors: Vincent Blondel
Gautier Krings
NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

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Scientific Committee:
Co-chair: Vincent Blondel
Co-chair: Alex (Sandy) Pentland
Rein Ahas
Samuel Arbesman
Laszlo Barabasi
Nicholas Christakis
Rob Claxton
Massimo Colonna
Nicolas de Cordes
Nathan Eagle
Kenth Engø-Monsen
Alexandre GerberMarta Gonzales
Cesar Hidalgo
Kimmo Kaski
János Kertész
Renaud Lambiotte
Juha LaurilaDavid Lazer
Franck Legendre
Esteban Moro Egido
Nuria Oliver
Jukka-Pekka Onnela
Dino Pedreschi
Daniele Quercia
Carlo Ratti
Jari Saramäki
Zbigniew Smoreda
John Tsitsiklis
Paul Van Dooren
Alexander Varshavsky

UCLouvain (Belgium) and MIT
Media Lab, MIT
University of Tartu (Estonia)
Harvard University
Northeastern University
Harvard University
British Telecom (UK)
Telecom Italia (Italy)
Orange Group Strategy (France)
txteagle
Telenor (Norway)
AT&T Research
MITMedia Lab, MIT
Aalto University (Finland)
Budapest University of Technology (Hungary)
University of Namur (Belgium)
Nokia Research (Switzerland)
Northeastern University
ETH Zurich (Switzerland)
Universidad Carlos III de Madrid (Spain)
Telefonica Research (Spain)
Harvard University
Università di Pisa (Italy)
University of Cambridge (UK)
Senseable City Lab, MIT
Aalto University (Finland)
Orange Labs (France)MIT
UCLouvain (Belgium)
AT&T Labs

Organizing Committee:
Chair: Vincent Blondel
Francesco Calabrese
Yves-Alexandre de Montjoye
Gautier Krings
Dashun Wang

UCLouvain (Belgium) and MIT
IBM Research (Ireland) and MIT
Media Lab, MIT
UCLouvain (Belgium)
Northeastern University

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Table of contents
Programp. 9

Abstracts session A

p. 19

Abstracts session B

p. 33

Abstracts session C

p. 47

Abstracts session D

p. 67

Abstracts session E

p. 79

Abstracts session F

p. 91

Abstracts session G

p. 105

campus map

p. 125

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Program overview

Monday October 10, 2011
8:30-9:00 Registration
9:00-9:15 Conference overview: V. Blondel
9:15-10:30 SESSION A: Individual mobility
10:30-11:00 Coffee break
11:00-12:15 SESSION B: Individual mobility
12:15-13:30 Lunch break
13:30-14:00 Keynote talk: A.-L. Barabási
14:00-15:45 SESSION C: Social networks
15:45-16:15 Coffee break
16:15-17:30 SESSION D: Population mobility
18:00 - 19:30 Dinner
starting at 19:30 Progressive Rock event

Tuesday October 11, 2011
9:30-10:30 SESSION E: Social structure
10:30-11:00 Coffee break
11:00-12:15 SESSION F: Dataset mining
12:15-13:30 Lunch break
13:30-14:00 Keynote talk: A. Pentland
14:00-15:30 SESSION G: Churn and value

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NetMob 2011
Second conference on the

Analysis of Mobile Phone Datasets and Networks

ProgramMonday October 10, 2011
8:30-9:00 Registration
9:00-9:15 Conference overview: trends in mobile phone network analysis
V. Blondel, UCLouvain

SESSION A (9:15-10:30). Individual mobility
9:15-9:30 Identifying Important Places in People’s Lives from Cellular Network
Data
S. Isaacman (1), R. Becker (2), R. Cáceres (2), S. Kobourov (3), M. Martonosi (1), J. Rowland (2), A.
Varshavsky (2)1. Princeton University
2. AT & T Labs - Research
3. Dept. of Computer Science, University of Arizona

9:30-9:45 Measuring repeated visitations with mobile positioning data. Applications for marketing.
R. Ahas (1), M. Tiru (2), A. Kuusik (3)1. Department of Geography, University of Tartu
2. Positium LBS
3. Faculty of Economics, University of Tartu

9:45-10:00 Classifying Routes Using Cellular Handoff Patterns
R. Becker, R. Cáceres, K., J. M. Loh, S. Urbanek, A. Varshavsky, C. Volinsky
AT&T Labs - Research

10:00-10:15 Characterizing Urban Road Usage Patterns With a New Metric
P. Wang (1,2), T. Hunter (3), A. Bayen (3), M. González (1)1. Department of Civil and Environmental Engineering, MIT

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

93. Department of Civil and Environmental Engineering, University of California Berkeley

10:15-10:30 Individual Mobility Networks from Clusters of Human Activity
S. Jiang (1), J. Ferreira (1), M. González (2)1. Department of Urban Studies and Planning, MIT
2. Department of Civil and Environmental Engineering, MIT

10:30-11:00 Coffee break

SESSION B (11:00-12:15). Individual mobility
11:00-11:15 Structure and seasonality in human motion
J. Bagrow (1,2), Y.-R. Lin (3,4)
1. Center for Complex Network Research, Northeastern University
2. Center for Cancer Systems Biology, Dana-Farber Cancer Institute
3. College of Computer and Information Science, Northeastern University
4. Institute for Quantitative Social Science, Harvard University

11:15-11:30 Exploring mobility of mobile users
B. Csáji (2,3), A. Browet (1), V. Traag (1), J.-C. Delvenne (1), E. Huens (1), P. Van Dooren (1), Z.
Smoreda (4), V.D. Blondel (1,5)
1. Department of Mathematical Engineering, Université catholique de Louvain
2. Department of Electrical and Electronic Engineering, University of Melbourne
3. Computer and Automation Research Institute (SZTAKI), Hungarian Academy of Sciences
4. Sociology and Economics of Networks and Services Department, Orange Labs
5. Laboratory for Information and Decision Systems, MIT

11:30-11:45 Chatty Mobiles: Individual mobility and communication patterns
T. Couronné, Z. Smoreda, A.-M. Olteanu
Sociology and Economics of Networks and Services department, Orange Labs R&D

11:45-12:00 Distance Matters: Geo-social Metrics for Mobile Location-based Social Networks
S. Scellato, C. Mascolo
Computer Laboratory, University of Cambridge

12:00-12:15 A tale of many cities: universal patterns in human urban mobility
A. Noulas (1), S. Scellato (1), R. Lambiotte (2), M. Pontil (3), C. Mascolo (1)1. Computer Laboratory, University of Cambridge
2. Department of Mathematics, University of Namur
3. Computer Science Department, University College London

12:15-13:30 Lunch break

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1013:30-14:00 Keynote talk: Human Dynamics and Cell Phones: From mobility to
predictability
A.-L. Barabási, Northeastern University

SESSION C (14:00-15:45). Social networks
14:00-14:15 Finding Meaningful Usage Clusters From Anonymized Mobile Call
Detail Records
R. Becker (1), R. Cáceres (1), C. Han (2), K. Hanson (1), J. M. Loh (1), S. Urbanek (1), A. Varshavsky(1), C. Volinsky (1)1. AT&T Labs - Research
2. Virginia Tech University

14:15-14:30 Using Cellular Network Data for Urban Planning
R. Becker, R. Cáceres, K. Hanson, J. M. Loh, S. Urbanek, A. Varshavsky, C. Volinsky
AT&T Labs - Research

14:30-14:45 Segmentation of towns using call detail records
R. Guigourès, M. BoulléOrange Labs

14:45-15:00 Understanding the evolution of spatial structure in urban communities
F. Walsh, A. Pozdnoukhov
National Centre for Geocomputation, National University of Ireland Maynooth

15:00-15:15 Trace-driven analysis of data forwarding in opportunistic networks
M. Karaliopoulos, P. Pantazopoulos, E. Jaho, I. Stavrakakis
Department of Informatics and Telecommunications, National & Kapodistrian University of Athens

15:15-15:30 Statistical detection of information flow traces in a large phone calldatasetL. Tabourier (1), F. Peruaniy (2)1. LIP6, Université Pierre et Marie Curie
2. Max Planck Institute for the Physics of Complex Systems

15:30-15:45 Socially-mediated diffusion via cell phones: an analysis of ringback
diffusion in a cell phone network
I. Kiani (1), D. Ruths (2), T. Shultz (3)1. John Molson School of Business, Concordia University
2. School of Computer Science, McGill University
3. Department of Psychology, McGill University

15:45-16:15 Coffee break

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SESSION D (16:15-17:30). Population mobility
16:15-16:30 Estimating Dynamic Urban Population Through Assimilated Mobile
Phone Data
T. Horanont (1), R. Shibasaki (2)1. Institute of Industrial Science, University of Tokyo
2. Center for Spatial Information Science, University of Tokyo

16:30-16:45 Modelling City Population Dynamics from Cell Phone Usage Data
Streams
C. Kaiser, A. Pozdnoukhov
National Centre for Geocomputation, National University of Ireland Maynooth

16:45-17:00 Exploiting cellular network MSC counters for the analysis of temporary populations
P. Tagliolato, F. Manfredini, C. Di Rosa
Dipartimento di Architettura e Pianificazione, Politecnico di Milano

17:00-17:15 Mobility and Predictability of Population Movements after the Haiti
2010 Earthquake
X. Lu (1,2), L. Bengtsson (1), P. Holme (3)1. Department of Public Health Sciences, Karolinska Institutet
2. Department of Sociology, Stockholm University
3. IceLab, Department of Physics, Umea University

17:15-17:30 Anonymizing Location Data Does Not Work
H. Zang (1), J. Bolot (2)1. Sprint
2. Technicolor

18:00 - 19:30 Dinner
starting at 19:30 Progressive Rock event at the Medialab in the 3rd Atrium.
Joe Paradisio will showcase amazing tech, including a 2 story video wall.

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Tuesday October 11, 2011
SESSION E (9:30-10:30). Social structure
9:30-9:45 Using Randomization Methods to Identify Social Influence in Mobile
Networks
R. Belo, P. Ferreira
Carnegie Mellon University

9:45-10:00 Quantifying and Modelling Good Communicators in Dynamic Phone
NetworksA. Mantzaris, D. Higham
Department of Mathematics and Statistics, University of Strathclyde

10:00-10:15 Examining the Social Decomposition of Mobile Call Graphs
D. Doran (1), V. Mendiratta (2), C. Phadke (2), H. Uzunalioglu (2)1. Dept. of Computer Science & Engineering, University of Connecticut
2. Bell Laboratories, Alcatel-Lucent

10:15-10:30 Time allocation in social networks: correlation between social structure and human dynamics
G. Miritello (1,2), R. Lara (2), E. Moro (1,3,4)
1. Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid
2. Telefónica Research
3. Instituto de Ciencias Matemáticas CSIC-UAM-UCM-UC3M
4. Instituto de Ingenierı́a del Conocimiento, Universidad Autónoma de Madrid

10:30-11:00 Coffee break

SESSION F (11:00-12:15). Dataset mining
11:00-11:15 Correlated bursty behaviour in human communication
M. Karsai (1), K. Kaski (1), A.-L. Barabási (2,3), J. Kertész (3,1)
1. BECS, School of Science, Aalto University
2. Center for Complex Networks Research, Northeastern University
3. Institute of Physics, Budapest University of Technology and Economics

11:15-11:30 Ethnic Segregation in the Area of Residence, Work, and Free-time
Evidence from Mobile Communication
O. Toomet (1,2), S. Silm (2), E. Saluveer (2), R. Ahas (2)1. Department of Economics, Tartu University
2. Department of Human Geography, Tartu University

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

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from Rwanda
J. Blumenstock (1), Nathan Eagle (2), M. Fafchamps (3)1. University of California, Berkeley
2. Santa Fe Institute
3. Oxford University

11:45-12:00 Composite Social Network for Predicting Mobile Apps Installation
W. Pan, N. Aharony, A. Pentland
MIT Media Laboratory

12:00-12:15 Mobile phone and motorway traffic: a panel data perspective
E. Tranos, J. Steenbruggen, P. Nijkamp, H. Scholten
Dept. of Spatial Economics, VU University

12:15-13:30 Lunch break

13:30-14:00 Keynote talk: Influence Networks
A. Pentland, MIT

SESSION G (14:00-15:30). Churn and value
14:00-14:15 Churn Analysis in Mobile Telecom Data using Hybrid Paradigms
V. Yeshwanth, M. Saravanan
Ericsson R & D

14:15-14:30 Relational Learning for Customer Churn Prediction: The Complementarity of Networked and Non-Networked Classifiers
W. Verbeke (1), T. Verbraken (1), B. Baesens (1,2,3)
1. Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven
2. School of Management, University of Southampton
3. Vlerick, Leuven-Gent Management School

14:30-14:45 Network neighbor effects on customer churn in cell phone networks
P. Krivitsky, P. Ferreira, R. TelangiLab, H. John Heinz III College, Carnegie Mellon University

14:45-15:00 Subscriber Behaviour in a Cellular Network implementing Dynamic
Pricing for Voice Calls
H. Wang, L. Kilmartin
Electrical & Electronic Engineering, National University of Ireland

15:00-15:15 What is the Economic Value of Cell Phone Location Data?
F. Baccelli (1), J. Bolot (2)NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

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2. Technicolor

15:15-15:30 Collective response of human populations to large-scale emergencies (invited talk)
J. BagrowCenter for Complex Networks Research, Northeastern University

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Workshop on the Analysis of
Mobile Phone Networks
MIT (Cambridge, USA)
October 10-11, 2011

Abstracts
Session A

p. 19

Session B

p. 33

Session C

p. 47

Session D

p. 67

Session E

p. 79

Session F

p. 91

Session G

p. 105

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Identifying Important Places in People’s Livesfrom Cellular Network Data
Sibren Isaacman⋄ , Richard Becker† , Ramón Cáceres† ,Stephen Kobourov⋆ , Margaret Martonosi⋄ , James Rowland† , Alexander Varshavsky†⋄
Princeton University, Princeton, NJ, USA
AT&T Labs – Research, Florham Park, NJ, USA⋆
Dept. of Computer Science, University of Arizona, Tucson, AZ, USA††⋄{isaacman,mrm}@princeton.edu{rab,ramon,jrr,varshavsky}@research.att.com⋆
kobourov@cs.arizona.edu

However, CDRs have two significant limitations as a sourceof location information. One, they are sparse in time because
they are generated only when a phone engages in a voice callor text message exchange. Two, they are coarse in space because they record location only at the granularity of a cell
tower. It is an interesting research question whether CDRs
can be used to identify important places in people’s lives.
We show that applying clustering and regression techniques
to CDR data can indeed identify important places in people’s
lives. First, we present an algorithm for identifying important places. Then, we describe two other algorithms for selecting home and work locations from among those important places. We validate all three algorithms by comparing
their results to ground truth provided by a group of volunteers. We then apply these algorithms to much larger anonymous populations in the Los Angeles (LA) and New York
City (NY) areas. Our LA and NY dataset spans two monthsof activity for hundreds of thousands of phones, yielding
hundreds of millions of location samples.
Finally, we present two example applications of these techniques. We start by using the home and work locations identified by our algorithms to calculate the distribution of commute distances per postal code in our Los Angeles and New
York dataset. We then estimate the carbon footprints of thosecommutes, also aggregated by postal code.

While people travel further and faster than ever before,
it is still the case that they spend much of their time at a
few important places. Identifying these key locations is thus
central to understanding human mobility and social patterns.
Such understanding can, in turn, inform solutions to largescale societal problems in fields as varied as telecommunications, ecology, epidemiology, and urban planning. As an example, knowing how large populations of people move about
would help determine their carbon footprint and in turn help
guide policies intended to reduce that footprint.
Wireless cellular networks hold great potential for providing the necessary information to identify important placesin people’s lives. The growing ubiquity of cellular phones
means that a large percentage of people keep a phone withthem most of the time. In addition, the networks need toknow roughly where each phone is in order to provide thephones with voice and data services.
In this work, we explore the use of anonymized Call Detail Records (CDRs) from a cellular network to estimate thelocations of important places in the lives of large populations of people. CDRs are routinely collected by cellular
network providers to help operate their networks, for example to identify congested cells in need of additional bandwidth. Each CDR contains information such as the time avoice call was placed or a text message was received, as wellas the identity of the cell tower with which the phone was associated at that time. This information can serve as sporadic
samples of the approximate locations of the phone’s owner.
CDRs are an attractive source of location information for
two main reasons. One, they are collected for all active cellular phones, which number in the hundreds of millions in
the US and in the billions worldwide. Two, they are alreadybeing collected to help operate the networks, so that additional uses of CDR data incur little marginal cost. Contrast
this low cost, for example, with the expense of carrying out
surveys to ask people where they spend their time. This highexpense generally limits other data collection methods to orders of magnitude fewer participants.

Privacy Measures.
Given the sensitivity of the data, we took several steps toensure the privacy of individuals in our CDR dataset. First,
only anonymous records were used in this study. In particular, personally identifying characteristics were removed
from our CDRs. CDRs for the same phone are linked using an anonymous unique identifier, rather than a telephone
number. Second, all our results are presented as aggregates.
No individual anonymous identifier was singled out for the
study. Finally, each CDR only included location information for the cellular towers with which a phone was associated at the beginning and end of a voice call or at the time
1

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Figure 1: True important locations vs. discovered important clusters for volunteers. Paddles are the important locations provided by the volunteers.
Circles are the important clusters discovered by our algorithm, with their radii signifying days of use. The four examples are drawn to the same scale.

ter to an important location and the βj s are coefficients thatare discovered during the regression. Once we determine thelikelihood that a particular cluster is “important,” we discard
clusters with a low probability of being important.
The effects of applying the Important Places algorithm
can be seen in Figure 1 along with true important locations
provided by volunteers. We come within 3 miles of the truth
for 88% of the important locations of our volunteers.

of a text message. The phones were effectively invisible tous aside from these events. Furthermore, we could estimate
the phone locations only to the granularity of the cell tower
coverage radius, which is often larger than 1 mile [5].
In addition to obtaining anonymized CDRs for hundredsof thousands of phones in LA and NY, we recruited several
dozen volunteers who gave us the true locations of important
places in their lives, as well as permission to inspect their
CDRs. An important place was defined as a location wherea person spends a lot of time or visits frequently.

Finding Home and Work.
We propose and evaluate two algorithms for applying semantic meaning to important locations, namely Home andWork, using models also derived via logistic regression. Fromthe important clusters identified by the Important Places algorithm described above, our Home and Work algorithms
select the clusters that correspond to where a person lives
and works, respectively. The algorithms are independent andHome and Work may end up in the same cluster.
To select Home or Work, the relevant algorithm (i.e., either the Home or Work algorithm) calculates a score for each
important cluster using coefficients obtained from a logistic
regression. The algorithm then assigns the cluster with thehighest score to be Home or Work. To calculate a score fora cluster, we use the logistic regression formula shown inEquation 1. In this case, P rob(x1 , ..., xn ) is the score calculated for each cluster, xi is the value of the ith factor and βj s
are regression coefficients fitted during training. Home andWork, then, are chosen as the clusters with the highest probability as computed by the coefficients given by the logistic
regression.
For the Home algorithm, the dominant factor is the cluster with the largest number of events during the “home”
hours, which is selected as Home. For the Work algorithm,
there are two dominating factors. The first is the rankingof the cluster based on the number of events occurring during “work” hours. The second factor is the percentage ofthe events occurring during “home” hours in the cluster. A
cluster is assigned a higher score by the Work algorithm if
the percentage of the home hour events in the cluster is low.
Once again, we apply our algorithms to a test set of volunteers. We estimate home and work locations with medianFinding Important Places.
We demonstrate an accurate and efficient algorithm for
identifying Important Places from CDRs.. Our algorithm
is the first to operate on the majority of cellular phone users,
rather than relying either on more continuous and fine-grained
tracking (e.g. GPS) or focusing on high-call-rate users whose
mobility is easier to analyze.
Our algorithm proceeds in two stages. In the first stage,
we spatially cluster the cell towers that appear in a user’s
trace. We pre-sort the towers in descending order according to the number of days a tower was contacted (“towerdays”), then we cluster them according to Hartigan’s leader
algorithm [3]. In the second stage, we identify which of theclusters are important using a model derived from a logistic
regression of volunteers’ CDRs.
To determine if a cluster is important, we use logistic regression. We considered five observable factors and ten derived variables. Of the 15 factors, three were statisticallysignificant. First, the percentage of total “tower-days” thiscluster represents. Second, the number of days between thefirst and last contact with any cell tower in the cluster. Third,
the number of days on which any cell tower in the cluster
was contacted compared to other clusters from this user.
P rob(x1 , ..., xn ) =1(1 + eβ0 +β1 x1 +···+βnxn )(1)Equation 1 shows the general form of the logistic regression formula that we use to estimate the likelihood of the importance of a cluster. In this formula, P rob(x1 , ..., xn ) is theprobability that a cluster with factors xi is the closest clus2

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

20(a) Los Angeles(b) New York

Figure 2: Heat maps of median carbon emitted per person for each direction of a commute in the ZIP codes in our study. Darker ZIP codes denotelarger carbon footprint. Note that all NY and LA ZIP codes are colored according to the same scale.

dioxide emitted per commuter. Aggregating commuters at
the ZIP code level allows us to generate a distribution of carbon dioxide emissions per commuter in each ZIP code.
Figure 2 shows heat maps of LA and NY, where shading corresponds to the median carbon emission per person
in each ZIP code in each direction of a commute. In the
New York area, increasing distance from Manhattan correlates with increasing carbon footprint. In contrast, Los Angeles is fairly uniform throughout, with the exception of certain parts of Antelope Valley (in the northeast part of themap), which are separated from downtown LA by a mountain range that must be driven around. These patterns match
well with what would be expected from both cities.
Generating carbon footprint estimates is a good exampleof how our technique for computing commuting distances
can be combined with already available data to produce newand previously difficult to obtain information.

errors of less than 1 mile.
Calculating Commutes.
We test our approaches on a dataset that is more universal
than prior work in several ways. First, it is simply larger thanprior work in terms of CDRs and number of users. Second, it
covers multiple distinct geographic areas. Third, it considers
users with a wide variety of call/text rates, from as low as afew calls/texts per week up to dozens of calls/texts per day.
We apply the Home and Work algorithms to two large
metropolitan areas in the United States (US): Los Angeles(LA) and New York (NY). By defining commute distance as
the distance as the crow-flies between home and work, we
compare commute distances as calculated by our Home andWork algorithms to those derived from US Census statistics.
In particular, we estimate the average commute distance forresidents of the 891 ZIP codes in our CDR dataset to be 21
and 20 miles for the Los Angeles and New York areas, respectively. Using tables of where people live and work published by the US Bureau of Transportation Statistics [2], we
calculate the average commute for residents of the same ZIP
codes to be 21 and 19 miles for the Los Angeles and New
York areas, respectively. This very close match between ouralgorithms and the census results further validates our approach. It is also important to note that the low cost of ourapproach makes it practical to regenerate current statistics
much more frequently than with a census, for example every
few months instead of every ten years.

Conclusion.
Overall, our clustering and location algorithms form a foundation for a range of accurate, low-overhead analyses of human movement and social patterns. Our work demonstrates
that call and text records from cellular networks represent
an unobtrusive and accurate way to gather large-scale mobility data. Furthermore, the large degree of aggregation andanonymization allows us to usefully employ this data without unduly impinging on the privacy of any individual. For
more details on our data set, our analysis methodology, andour results, we refer the reader to our full paper [4].

Carbon Footprints.
Finally, we show of how technology providers and policy
makers might use our algorithms in their work. In particular,
we combine the commute numbers we have calculated withpublicly available data to estimate the carbon footprints of
those commutes in two major metropolitan areas.
We determine the mode of transportation of commutersat the ZIP code level using US census data. Specifically,
we used Table P30 from the 2000 US census (Summary
File 3): “Means of Transportation to Work for Workers 16+
Years.” [6] to calculate the percentage of commuters thatuses a particular mode of transportation per ZIP code. Further, combining this information with the amount of carbon dioxide emitted per person by each mode of transportation [1] allows us to compute the rough amount of carbon

1.
References[1] M. J. Bradley and Associates. Comparison of energy use & CO

2emissions from different transportation modes. Report to American
Bus Association, 2007.
[2] US Bureau of Transportation Statistics. Downloaded fromhttp://www.transtats.bts.gov.
[3] J. A. Hartigan. Clustering Algorithms. John Wiley & Sons, New York,1975.
[4] S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, M. Martonosi,
J. Rowland, and A. Vasharsvky. Identifying important places inpeoples lives from cellular network data. In Intl. Conference on
Pervasive Computing, 2010.
[5] C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits ofpredictability in human mobility. Science, 327, 2010.
[6] US Census Data. Downloaded from http://www.census.gov.

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Measuring repeated visitations with mobile positioning data. Applications formarketing.
Rein Ahas1, Margus Tiru2, Andres Kuusik3
1

Department of Geography, University of Tartu , rein.ahas@ut.ee
Positium LBS, 9 Õpetaja St., Tartu , margus.tiru@ut.ee
3
Faculty of Economics, University of Tartu, andres.kuusik@ut.ee
2

Abstract
The aim of this paper is to elaborate a mobile positioning based methodology to measure
repeated visitations and place loyalty. Repeated visitations show behavioural loyalty ofpeople, ties to certain places, attractions or events. Loyal visitors are highly appreciated inmarketing, since they are of greater benefit than random customers; they need different
marketing strategies than those required to attract new visitors.
We developed a model to determine repeat visits of tourists and domestic people to a
destination using passive mobile positioning data. Model uses the number, duration,
frequency and geography of visits in order to identify repeat visitors in a particular area. We
tested the algorithm using a database of foreign and local visitors during past 5 years. The
algorithm was applied in destination marketing solutions for Estonian tourism authorities.
Further, it has potential for various LBS can solutions.

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1

Classifying Routes Using Cellular Handoff Patterns

0.8
0.6
0.2

0.4

accuracy

EMD
common antennas
common sectors
common towers

0.0

Urban planners are interested in understanding the mobility
patterns of the people who live in and use their cities. This
understanding facilitates effective solutions to problems withtraffic congestion, parking, vehicular and pedestrian safety,
and other aspects of urban living. To gain some knowledgeof mobility patterns, planners currently use a combination ofcensus data and vehicle counting. However, the expense of
these methods typically results in infrequent data collection
and/or small population samples.
Cellular telephone networks have the potential to provide
near real-time information about human mobility on a large
scale and at a low cost. These networks must know theapproximate locations of all affiliated cell phones in order toprovide the phones with voice and data services. Since people
usually carry their cell phones with them, the location of aphone is a good proxy for the location of its owner.
This abstract explores the use of cellular handoff patterns
to identify which routes people take into and out of a city.
A handoff pattern is the sequence of cellular antennas thata moving phone uses while engaged in a voice call. The
main challenge in translating these patterns into useful routeinformation is location inaccuracy due to the large geographic
areas covered by individual antennas: often more than one
square mile. Therefore, knowing which antenna a phone isconnected to does not immediately reveal what route thephone’s owner is traveling on. However, it is possible thatknowing sequences of antennas can yield enough information
to reveal these routes.
In this abstract, we investigate the use of handoff patterns extracted from anonymized Call Detail Records (CDRs).
CDRs are collected when a phone is involved in a call, andmay contain the full sequence of antennas used by the phone
during that call. CDRs are routinely collected by network
operators for all active cellular phones, which number inthe hundreds of millions in the US and billions worldwide.
Furthermore, CDRs are already used for network operationand planning, so additional uses incur little marginal cost.
Another advantage of CDRs is that they are generated inside
the network and thus do not place any extra burden on thelimited resources of mobile phones. We note, however, that ourroute identification techniques are independent of how cellularhandoff patterns are recorded, whether by the network or by
the phones.
Our work explores the following research questions: (a)Are handoff patterns stable across a wide enough range ofconditions to be used for identifying routes? (b) Can we devise
algorithms that reliably match handoff patterns to routes? (c)1.0Richard A. Becker, Ramon Caceres, Karrie Hanson, Ji Meng Loh,
Simon Urbanek, Alexander Varshavsky and Chris Volinsky
AT&T Labs - Research{rab, ramon, karrie, loh, urbanek, varshavsky, volinsky} @research.att.com

5

10

15

20

handoffs

Fig. 1. A comparison of nearest-neighbor classification using four distancemetrics, with four handoff patterns as training and four as testing per route.
Accuracy is shown as a function of the number of handoffs per drive. The
boxplots show the range of accuracy over 10 randomly chosen training sets
and the colored lines connect the medians of those sets. The EMD metricout-performs the others.

Can we derive reliable route utilization statistics from cellular
network data?
To answer these questions, we conducted a study of 15
driving and train routes leading into Anytown, a suburban
city with roughly 20,000 residents. We used cellular phones
to maintain active voice calls while we drove a car and rodethe train on these routes. Then, we used the corresponding
handoff patterns (obtained from CDRs) to evaluate different
route classification techniques. Finally, we applied our best
performing techniques to 60 days of anonymized CDRs for all
calls handled by one cellular carrier in the Anytown area. Dueto the lack of space, we only summarize our key contributions
here. Please see our full paper [1] for details.
A. Stability of Handoff Patterns
We found empirically that cellular handoff patterns on agiven route are stable across phone models, weather conditions, traffic conditions and time. This stability allows captur-

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

23ing the “typical” handoff pattern for a given route at one timeand use it for route classification at another time.

EMD is traditionally used to measure the differences between
images. Conceptually, imagine a pair of two dimensional
images, where each pixel’s brightness value is represented
by a pile of dirt on that pixel. Pixels with similar brightness
have similar amounts of dirt. Now, consider the energy needed
to transform one image into the other by moving the pilesof dirt. EMD is defined as the minimal energy needed tomove the mass of dirt of one image into the locations that
result in the target image. Similarly, EMD is defined forarbitrary probability distributions as the mass of probability
that needs to be moved to turn one distribution into another.
Applying EMD in a nearest-neighbor classification task is astraightforward calculation of EMD between the test instances
and those in the training set, then selecting the route label ofthe nearest one.
3) Performance of the Classification Algorithms: We areinterested in measuring how much information is needed ona single drive to classify it correctly. Since all of our routes
emerge from the center of Anytown, we assessed the accuracyof our algorithms for a call starting in the center of Anytown as
a function of the number of handoffs the algorithm is allowed
to observe. To generate results, we split our data into training
and test sets, with four randomly selected drives for each routein each set. We fit each test instance to its nearest neighborin the training set using our four distance metrics, EMD andthe three common subset distances, repeating this procedurefor 10 different random selections of the training set.
Figure 1 shows our results, with boxplots representing theaccuracy across all replications for each distance metric andnumber of handoffs. Colored lines connect medians of theboxplots. The figure shows that, in general, the prediction
accuracy increases as the number of handoffs increases up
to a saturation point for all metrics. That is, the farther away
the phone moves from the Anytown center, the easier it is
to differentiate between routes. The EMD metric performs
the best, achieving a median classification accuracy of 90%
after 12 handoffs (corresponding roughly to 2 miles). The
Common Towers metric performs the worst because it cannot
differentiate between handoffs occurring between antennas
on the same cell tower. Interestingly, the Common Antennas
metric outperforms the Common Sectors metric for up to 10
handoffs, but then performs worse because sometimes phone
calls on the same route are handled by different antennas
pointing in the same direction.

B. Route Classification Algorithms
We developed two algorithms for matching handoff patterns
to routes and showed that they have comparable accuracy.
The first uses nearest neighbor classification based on EarthMover’s Distance [3]. The second uses signal strength data tocompute the likelihood that a given handoff pattern occurs on
a particular route. Due to lack of space, we discuss only the
first algorithm in this abstract.
We collected data on 13 commuter routes and 2 train routes
leading into Anytown. These routes represent all major ways
to get in and out of the city. The route lengths vary from 3
to 6 miles. We travelled each route four times, two in eachdirection, primarily in the Fall of 2010, with a few fill-in drives
and train rides in March of 2011. During each drive, there
were two phones of different models in the car, one callingthe other. In total, we collected 4 × 2 × 15 = 120 handoff
patterns.
Our classification is done via a nearest-neighbor algorithm.
For each route, we split the 8 test drives randomly into equal
sized training and test sets. For each instance in the test set, weassign the route label of the nearest instance from the training
set. The choice of distance metric is crucial for determining
the nearest neighbor. In the following three subsections, we
describe the four distance metrics we evaluated and discuss
their relative performance.
1) Common Subset Distances: Distances between two
handoff patterns can be defined by measuring how much thetwo patterns have in common. These distances are based on
attributes of antennas in the handoff patterns. The larger theintersection between these sets of attributes, the more similar
the handoff patterns. We refer to these distances as commonsubset distances. We defined three common-subset distancesthat compare these attributes at different levels of granularity:cell towers, sectors, and antennas. A cell tower is a physicalstructure that holds radio antennas. A sector corresponds to a
direction from a given cell tower. Each sector is covered by one
or more antennas, the physical devices that communicate withmobile phones and service a particular cellular technology(e.g., UMTS) and frequency (e.g., 2.1 GHz). The Common
Antennas distance between two handoff patterns is the numberof antennas that appear in both handoff patterns. Similar
definitions apply to Common Sectors and Common Towers
distances.
2) Earth Mover’s Distance: Although common subset distances are good for baselines, they do not account for threeimportant characteristics of the handoff pattern. First, thesequential nature of the handoff pattern is lost, basicallyreduced to an unordered set of entities. Second, temporal
information on how long the call spends on each tower is not
used by these algorithms. Finally, the cell tower locations are
not accounted for. Two patterns that differ only by towers thatare close to each other should be considered close patterns.
We propose a variant of Earth Mover’s Distance (EMD) as
a distance metric that accounts for all of these characteristics.

C. Estimating Relative Traffic Volumes
We showed how CDRs, in combination with our algorithms,
can be used to determine the relative traffic volumes on roads
and validate these results against statistics published by a statetransportation authority.
1) CDR Data Collection: We collected anonymized CDRs
from calls carried by the 35 cell towers (about 300 antennas)
located within 5 miles of the center of Anytown. Our goal wasto capture cellular traffic in and around the city and choosing
the 5-mile radius allowed us to cover both Anytown proper andits neighboring areas. We collected voice traffic for 60 days
between November 29, 2009, and January 27, 2010, resulting
in 15 million voice CDRs for 475,000 unique phones.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

2

24

2500

R1
R13
R3R2
R4

0

500

120

105

R11

1500

43

R12
R6

1000

32

EMD Estimated Calls

39

25

correlation: 0.77
linear fit: y = 0.106 x

2000

252

126

0

10000

15000

20000

DOT Traffic Count Estimates

17
37

5000

67

Fig. 3. Comparison of traffic volumes estimated by our EMD algorithm
against corresponding values obtained from the Anystate Department ofTransportation.

131

Fig. 2. Predicted traffic distribution of 1000 calls. The EMD algorithm wasused to estimate traffic volumes on the 13 commuter routes into Anytownwhich are shown as flows into Anytown, with the line width proportional tothe estimated volume; the Signal Strength algorithm results are similar.

counters. Both methods give a count at a static point, while ourmethods are estimating traffic along a particular route madeup of many sequential points.
Nonetheless, we attempted to validate our data with available traffic count information from the Anystate Departmentof Transportation (DOT). The DOT has a multitude of data
available at strategic places around town from traffic meters
over the years 2004-2010 [2]. Most of our routes had multiple
traffic measurements available at different locations along theroute.
Because Anytown is a local hub, we made the simplifying
assumption that all of the traffic on the secondary roads
heading into town either originated or terminated at the town
center. Since this assumption might not hold for the maininterstate running through town, we did not include thoseroutes in this analysis. Also, we did not have any traffic counts
for any part of one of our routes, so we excluded that routeas well. Additionally, if a route had multiple measurements ofdaily traffic at different locations, we selected the minimumof these counts. In most cases, this minimum count was at thefurthest point from Anytown, and hence was the best estimateof the number of cars that had travelled the entire length of
the route. Finally, we removed a single data point from the
DOT data that was highly suspicious as an outlier; it wasincongruous with nearby data points in a way that made itappear to be erroneous. Figure 3 shows a scatterplot of EMDestimated traffic counts vs. the DOT-supplied traffic counts.
The figure shows that the two estimates are closely related,with a correlation coefficient of 0.77. We believe that thisresult validates our methodology.

2) Privacy: Given the sensitivity of CDR data, we took
several steps to ensure the privacy of individuals. First, only
anonymous records were used in this study. The data wascollected and anonymized by a party not involved in the data
analysis. Personally identifying characteristics were removed
from our CDRs. CDRs for the same phone are linked using ananonymous unique identifier, rather than a telephone number.
No demographic data is linked to any cell phone user or CDR.
Second, all our results are presented as aggregates. That is, no
individual anonymous identifier was singled out for the study.
By observing and reporting only on the aggregates, we protect
the privacy of individuals. Finally, each CDR only included
location information for the cellular antennas associated witha phone during a voice call. The phones were effectively
invisible to us outside those times, and we only knew thosephone locations at the granularity of an antenna’s coveragearea, often greater than one square mile.
3) Analysis: Figure 2 plots relative traffic volumes asestimated by the EMD algorithm, overlaid on our map ofroutes into and out of Anytown. The counts are normalized
to a count per 1000 calls. The thickness of the line represents
the volume estimated on that route. The plot allows comparing
the relative number of people who access the town from northand south on the interstate (the black lines) vs. the relative
number of people who enter and leave Anytown on secondary
state or county roads.
4) Validation: We present our results as relative volumes
instead of absolute volumes since there are many factors thatplay into whether we will see a particular traveling vehicle:
the phone must be active, the user must be a customer of thecellular provider that supplies the data, the call must use fiveunique towers (to ensure that it is moving, not stationary),and so on. Because of this, validation of our numbers against
readily available government traffic count data is challenging.
An additional challenge is that the government traffic data is
typically collected using in-street traffic meters or human car

R EFERENCES[1] R. Becker, R. Cáceres, K. Hanson, J. M. Loh, S. Urbanek, A. Vasharsvky,
and C. Volinsky. Route classification using cellular handoff patterns.
In Proc. of the 13th ACM International Conference on Ubiquitous
Computing, 2011.
[2] NJ Department of Transportation. Roadway information and traffic
counts. www.state.nj.us/transportation/refdata/roadway/traffic.shtm.
[3] Y. Rubner, C. Tomasi, and L. J. Guibas. The earth mover’s distance as a
metric for image retrieval. Int. J. Comput. Vision, 40:99–121, November
2000.

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325

Characterizing Urban Road Usage Patterns with a New Metric
Pu Wang1,2, Timothy Hunter3, Alex Bayen3 and Marta C. González1
1

Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139,
USA

2

School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, 410000, P.R. China

3

Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA

Email: Pu Wang: puwang@mit.edu, Timothy Hunter: tjhunter@eecs.berkeley.edu, Alex Bayen: bayen@berkeley.edu,
Marta C. González: martag@mit.edu

Global communication through mobile phones is a massive phenomenon in urban centres from theentire planet. This generates petabytes of information that contains fingerprints of individual humanactivity from remote locations. This sea of information has revolutionized the opportunities for sensing
and modelling human travel, offering us a real-time large scale ‘survey’ incessantly recording people’s
everyday communication (1), community (2) and mobility information (3, 4). Comparing to the survey
and traffic count data, mobile phone data is less expensive and widely available in most regions. Theseadvantages have attracted many attentions in using mobile phone data to study urban transportation, yetmost of the works only estimated relatively small OD matrices (5, 6) on some small or conceptual road
networks (7, 8). To date it is still missing the link to quantify the interactions between the individual
mobility extracted from mobile phone data and the real and detailed road infrastructures at a city or evenlarger scale.

We use mobility data from half million anonymized mobile phone users to study the road usage
patterns in San Francisco Bay Area. Through our modelling framework each trip’s route is predicted andrecorded, surprisingly, we find that averagely 60% of vehicles passing through a road segment comefrom 1% of Bay Area drivers’ home locations, suggesting a high predictability of vehicle sources. For
each road segment we apply Gini coefficient to quantify the heterogeneous traffic contribution by
drivers living in different neighbourhoods. We find that Gini coefficient ranges from 0.77 to 1, hinting

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

26

that a road segment’s traffic flow is highly unequally produced by drivers living in different
neighbourhoods. Counter intuitively, a road segment’s Gini coefficient is lowly correlated with its basic
properties such as traffic volume and volume over capacity, suggesting that Gini coefficient is a new
metric on top of the traditional measures, quantifying road usage patterns in the perspective of drivers’
demographical distribution.

REFERENCES:
1. J.-P. Onnela et al., Proc. Natl. Acad. Sci. U.S.A. 104, 7332 (2007).
2. G. Palla, A.-L. Barabási, T. Vicsek, Nature 446, 664 (2007).
3. M. C. González, C. A. Hidalgo & A.-L. Barabási, Understanding Individual Human Mobility Patterns. Nature 435,779-782 (2008).
4. C. Song, Z. Qu, N. Blumm & A.-L. Barabási, Limits of Predictability in Human Mobility. Science 327, 1018-1021(2010).
5. N. Caceres, J.P. Wideberg & F.G. Benitez, Deriving origin–destination data from a mobile phone network, IET
Intell. Transp. Syst., 1, (1), pp. 15–26 (2007).
6. J. Schlaich, T. Otterstätter & M. Friedrich, Generating Trajectories from Mobile Phone Data, Transportation
Research Part B Annual Meeting CD-ROM (2010).
7. C. Ratti, D. Frenchman, R. M. Pulselli & S. Williams, Mobile Landscapes: using location data from cell phones forurban analysis, Environment and Planning B: Planning and Design 33, 727 -748 (2006).
8.

Y. Zhang, X. Qin, S. Dong & B. Ran, Daily O-D Matrix Estimation using Cellular Probe Data, Transportation
Research Part B Annual Meeting CD-ROM (2010).

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27

Individual Mobility Networks from Clusters of Human Activity
Shan JIANG 1, Joseph FERREIRA 1, Marta GONZÁLEZ 2
1
2

Department of Urban Studies and Planning, MIT, Cambridge, MA

Department of Civil and Environmental Engineering, Cambridge, MA
Email: shanjang@mit.edu, jf@mit.edu, martag@mit.edu

Introduction
Recent innovation in both data sources and analytical approaches have inspired new studies
about the dynamics of human activities (Gonzalez et al. 2008; Song et al. 2010). While
deploying these new sources of data (such as mobile phone data), researchers have been facing
significant challenges—due to privacy and legal constraints, no or very limited information onsocioeconomics and demographics of the human subjects being studied and/or types of activities
being conducted can be accessed in these circumstances. Despite the fact that these new datasets
may allow researchers to study social relationship and networks (Eagle et al. 2009), they still
have limited capacity in revealing underlying reasons driving human behavior (Nature Editorial
2008).
Compared with the aforementioned urban sensing data, survey data is disadvantaged by high cost,
low frequency, and small sample size. However, in terms of socioeconomic and demographic
information, survey data provides much richer and detailed information. We exploit the richnessof survey data using data mining techniques, which have not been applied in this context before.
Since the survey collected over the metropolitan area is conducted by the metropolitan planning
organization (MPO) for the regional transportation planning purposes, it is a representative
sample of the total population of the region (Chicago Metropolitan Agency for Planning 2008).
In this study, we integrate and compare mobile phone activity with travel survey information,
both collected in the Chicago Metropolitan Area.

Methodology
We first perform a time series analysis of each of the 30,000 surveyed individuals, dividing theentire day into 288 five-minute intervals; for each time interval we know the type of activity an
individual is performing. We divide the self-reported activities into the following nine
categories: (1) Home, (2) Work, (3) School, (4) Transportation Transitions, (5) Shopping/
Errands, (6) Personal Business, (7) Recreation/Entertainment/Friends, (8) Civil/ Religious, and(9) Other. To detect characteristic groups, we compute the principal components of the temporal
activity matrix (30,000 x 2,592), in which each row represents an individual, and the columns are
the 288 five-minute intervals of a day combined with the 9 types of activities.
The differences between our study and the traditional time-use studies on human activities lie in
our methods. We do not superimpose any predefined social classification on the observations; we
1
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28

group the results with K-means clustering. We let the inherent activity structure inform us of thepatterns and clusters of individual temporal activities in the metropolitan area (Jiang et al. 2011).
Figure 1 exhibits the K-means clustering results (with K=8) of individuals’ temporal activity
patterns on an average weekday along with the social demographic characteristics of thoseindividuals grouped into each cluster based on their activity signatures.

Figure 1: Clusters of individuals’ weekday temporal activity patterns.
[Note: The color bar indicates the nine reported activity types (see previous text). Left panel shows theactivity matrix of each cluster, each row is an individual one-day itinerary, the columns representing timeof day and colors indicating the activity. Central panels show the percentage of individuals performing aparticular activity vs. time of day, and left side plots is a zoom from the one on the right— the colors ofeach curve represent the activity types. Right panel represents the name assigned to the cluster given its
particular behavior and the percentage of total population belongs to it.]

From each cluster we construct individual mobility networks (see examples in Figure 2). These
networks define dynamic networks in which nodes represent the location of a particular activity
and the links the trips between them. We extract the signatures of eight network groups that canbe used to model urban trips. The first challenge is to see if we may find characteristic networks
within each cluster that can be expressed with simple dynamic rules. Next, we explore individual
mobility networks as extracted by the phone users and use the findings of the survey to try tocomplete some aspects of the daily activity of the mobile phone users. By placing those networks
in real space, one can build the aggregated mobility network or OD matrix of the entire city.

2
NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

29

(a)

(b)

(d)

(e)

(c)

(f)

Figure 2 Examples of individual mobility networks of (a) an early-bird worker, (b) a regular
worker, (c) a student, (d) an afternoon worker; (e) a morning adventurer, and (f) an afternoon
adventurer. [Note: The examples of individual mobility network are extracted from samples of the 8classified clusters defined in Figure 1 from the survey data. Nodes in each graph represent activity
destinations, colors represent the activity types, and sizes of the nodes are proportionate to activity
duration at a particular destination.]

Research Contributions
Similar data mining techniques, to the ones here proposed have successfully being applied tocharacterize human behavior, as for example, the extraction of patterns of temporal variations in
on-line media content(Yang and Leskovec 2011) and activities within a college campus (Eagle
and Pentland 2009). Daily activity data of groups of individuals in a city must have underlying
structures that can be extracted using similar techniques. K-means and eigen decompositions
combined with network analysis are particularly useful because they provide a low dimensional
characterization of complex phenomena. The obtained classification related to the spatial
information of the activities will provide clear information on where, when and how theindividuals interact with places in the city, providing a clear framework for urban andtransportation planning.

3
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30References
Chicago Metropolitan Agency for Planning. 2008. Chicago travel tracker household travel inventory(2007).
Eagle, N., and Pentland, A., 2009. Eigenbehaviors: Identifying structure in routine. Behavioral Ecology
and Sociobiology, 63 (7), 1057‐1066.
Eagle, N., Pentland, A., and Lazer, D., 2009. Inferring friendship network structure by using mobile phonedata. Proceedings of the National Academy of Sciences.
Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.‐L., 2008. Understanding individual human mobility
patterns. Nature, 453 (7196), 779‐782.
Jiang, S., Ferreira, J., and Gonzalez, M., 2011. Understanding the link between urban activity destinations
and human travel patterns Paper presented at the Computers in Urban Planning and Urban
Management 2011 Conference, Lake Louise, Canada.
Nature Editorial. 2008. A flood of hard data. Nature, 453 (7196), 698‐698.
Song, C., Qu, Z., Blumm, N., and Barabási, A.‐L., 2010. Limits of predictability in human mobility. Science,
327 (5968), 1018‐1021.
Yang, J., and Leskovec, J., 2011. Patterns of temporal variation in online media. In: Proceedings of the
fourth ACM international conference on Web search and data mining, Hong Kong, China: ACM.

4
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NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

32Structure and seasonality in human motion
James P. Bagrow1,2,∗ and Yu-Ru Lin3,4,†
1

Center for Complex Network Research, Northeastern University, Boston, MA 02115.
Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
3
College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA
4
Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA∗†bagrowjp@gmail.com
yuruliny@gmail.com
2

July 15, 2011
Recently a number of intriguing results have emerged regarding the long-term properties of humanmotion, such as its high predictability and ultra-slow diffusive growth. Using a country-wide mobile
phone dataset, we wish to explore the underlying structures of human motion. We follow a populationof approximately 80,000 users over a 30-week period, using their individual trajectories to extract amobility network between cellular towers. These networks show highly non-random structure, fromheavily clustered central locations to long chains and loops. The evolution of the distribution of user
relocations over long times allows us to explore seasonal effects.

Interest in human mobility has exploded due to the recent availability of new cutting edge datasets, suchas dollar bill-tracking websites [1] and cell phone call records [2]. It has been shown that people can moveover very broad spatial ranges. It is also known that people are very recurrent in their motion, showing
strong daily periodicities; users are highly predictable [3]. Models have emerged to explain the ultraslow
exploratory growth of human mobility. Interestingly, it has also been shown that a simple Markovian random
walk can well approximate how people move between knowns locations [4]. (This paradoxical result is
resolved when one realizes that this random walk requires a data-dependent transition matrix, and is notcapable of generating it on its own. For that one needs a proper generative model, such as [5].)We wish to explore further the underlying causes of these phenomena. Are their long-term seasonalities
in human motion? What are the structures of such trajectories? For example, if users cluster around specificlocations such as home and work, how often do they move between these clusters? [6] Do there exist extended periods of motion away from these clusters, where users anomalously prefer less visited—and thus
less predictable—locations? Do users move more or less frequently during winter compared with summer?
What demographic effects are present, such as due to age or gender?
We begin with a mobile phone dataset, from which we sample approximately 80k users, following thecriteria of [3]. We follow each user’s trajectory over a period of 30 weeks, considerably longer than previous
studies. Each user i provides a trajectory τi based on the time-ordered sequence of cellular towers they visit,τi = [L(t1 ), L(t2 ), . . .], where L(ti ) represents the location that user i visited at time ti . We take time at anhourly resolution. In Fig. 1(a) we draw an example mobility network, where each node represents a cellular
tower and links represent relocations from one tower to another. Highly non-random structures such as
several cores and a number of long-range, low-degree loops are present. In Fig. 1(b) we study the degree
distributions of these Mob-Nets. They are well approximated by a power-law with an exponential cutoff. In
Fig. 1(c) we show the temporal evolution, as a function of time of year and hour of week, of the call patterns

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33

100

10-1

10-1

10-1

10-2

10-2

10-2

10-3

10-3

10-3
10

-4

10

-5

β = 1.45
κ = 23.4

10-6
10

10

-4

10

-5

10
0

10

1

10

β = 1.44
κ = 16.8

10-6

-7

10

Pr(kin)100

Pr(kout)

Pr(k)

100

2

week of the year
week of the year
week of the year

4

x 10

10

5

20
30
48

72

96

120

hour of the week
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144

168

0
4

x 10
4

10

2

20
30
24

48

72

96

120

hour of the week
Number of relocations per call

144

168

0

0.6
10
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20

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48

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10

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10

β = 1.54
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2

kout

-7

10

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10

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kin

(b)

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24

10

10
0

k
(a)

-4

10-6

-7

10

10

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96

hour of the week

120

144

168

Figure 1: Spatiotemporal properties of humanmobility. (a) An example mobility network (MobNet), drawn without using spatial coordinates. Several dense cores are clearly visible, as are a number of unusually long loops, representing one-time
trips. (b) Degree distributions (undirected, outdegree, and indegree) for all Mob-Nets. They are welldescribed by a power law with an exponential cutoff, Pr(k) ∼ k−β e−k/κ . (c) Averaging over the whole
population we study the number of calls and numberof relocations as a function of time of the week andweek of the year. The ratio of the two gives the fraction of calls that lead to relocation. We see a strong
daily rhythm, with weekends having lower activity
but more nightly relocations than weekdays.

(c)and relocations for the entire sample. We see a striking weekly pattern, whereas there is little dependenceon time of year, indicating that human mobility is relatively independent of weather or temperature effects,
at least for this population. We also observe that users move more frequently during weekend nights than
weekday nights.
The wealth of new data recently available opens avenues for a number of studies of human mobility,
as well as the interplay between mobility, demographics, and social network effects. Further work includes
quantifying the effects of age and gender on human mobility, and understanding how mobility correlateswith social network effects such as the number of friends.
[1] D. Brockmann, L. Hufnagel, and T. Geisel. The scaling laws of human travel. Nature, 439(7075):462–465, 2006.
[2] M.C. González, C.A. Hidalgo, and A.L. Barabási. Understanding individual human mobility patterns. Nature, 453(7196):779–
782, 2008.
[3] C. Song, Z. Qu, N. Blumm, and A.L. Barabási. Limits of predictability in human mobility. Science, 327(5968):1018, 2010.
[4] J. Park, D.S. Lee, and M.C. González. The eigenmode analysis of human motion. Journal of Statistical Mechanics: Theory
and Experiment, 2010:P11021, 2010.
[5] C. Song, T. Koren, P. Wang, and A.L. Barabási. Modelling the scaling properties of human mobility. Nature Physics, 2010.
[6] J.P. Bagrow and T. Koren. Investigating bimodal clustering in human mobility. In International Conference on Computational
Science and Engineering, 2009. CSE’09., volume 4, pages 944–947. IEEE, 2009.

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1

E XPLORING M OBILITY OF M OBILE U SERS
B.Cs. Csáji† ‡ , A. Browet∗ , V.A. Traag∗ , J.-C. Delvenne∗ , E. Huens∗ ,P. Van Dooren∗ , Z. Smoreda§ , V.D. Blondel∗¶∗ Department of Mathematical Engineering, Université catholique de Louvain, Belgium† Department of Electrical and Electronic Engineering, University of Melbourne, Australia‡ Computer and Automation Research Institute (SZTAKI), Hungarian Academy of Sciences§ Sociology and Economics of Networks and Services Department, Orange Labs, France¶ Laboratory for Information and Decision Systems, MIT, Cambridge (MA), USA

I. I NTRODUCTION
Mobile phone technology has been an important source ofinspiration to sociology, especially these last years. Firstly,
because it influences the behavior of people, which is a subjectof study in itself (e.g. [1] [5]). Secondly, because they can beused to investigate human behavior. Finally, they are useful forthe providers of these technologies, most notably for marketing
purposes. One example of this is the detection of ‘social
leaders’ [2].
More recently, the mobile phone data available to researchers have been enriched with new information: the position of the antenna used by the caller/receiver. This allows
us to know the approximate position of people when they
give/receive a call. For instance, it has been exploited tocheck assumptions on the statistical laws governing mobilityof people in their everyday life [3]. In that research the mobile
phone is only a detector of position, and no use is made of
the social network of the user.
Here we present work based on mobile phone data enrichedwith geographical information. We first perform a statisticalanalysis of the data in order to provide a better understandingof it. We observe that most people’s mobility is concentrated
on a small set of geographical locations. Then we introduce
a probabilistic model aimed at the analysis of these frequent
locations and the understanding of their temporal dynamics.
II. S TATISTICAL ANALYSISThe anonymous data, obtained from a mobile phone company, include all the communications between the mobile
phones of this company in Portugal, over a period of 15
months. For every communication, we know the time ofinitiating and ending it; which users were involved; what
antenna picked it up from the sender; and what antenna
relayed it to the receiver. In addition, we also know the exact
coordinates (longitude, latitude) of all antennas.
The antenna relaying the communication is not necessarily
the nearest one. Therefore, the data contain some noise. For thestatistical analysis, we have filtered out this noise by applying

(1)

(2)

(a) gyration or standard deviation: the meansquare error from the average location of
the user (i.e., the center of the circle).

(b) diameter of the convex hull: the maximal
distance the user has traveled during the
given period (the diameter of the set).
(c) total line segment length: the sum of all the
distances the user has travel between his
calls (the time of the calls are important).

Figure 1. (1) The average locations of the users. Brighter colors indicatethat a higher number of users have their average locations in that area. (2)Three different ways to measure the size of the movements of a customer.

a linear weighted moving average. We then define fifty features
based on different aspects of users’ behavior, such as: (1)time patterns of the calls (average duration, etc.); (2) location
patterns of the user (most frequently used antenna, etc.); (3)location patterns of the correspondent (average distance ofthe correspondent, etc.); (4) user statistics (number of calls,
number of callers, etc.); and (5) mobility statistics (gyration,
diameter of convex hull, etc.). These mobility features are
displayed in Fig. 1, next to a map showing the density ofusers across the country.
We first perform a basic correlation analysis. It appears thatthe data are highly redundant and can be presented much
more efficiently. In particular, by selecting only five newfeatures, combinations of the original ones by using principle
component analysis (PCA) or clustering analysis, a negligible
amount of information is lost. Those five new features aremostly determined by location and mobility patterns, thepositions of the two most frequently used antennas being
especially important. This points to the fact that users spend
most of their time in only a few locations.
III. F REQUENT L OCATION A NALYSISIn order to investigate these frequent locations, we performed a more detailed analysis. Often, various antennas could
be used for calls made in a single location, due to noise on
the signal, such as reflections, buildings, etc. Therefore, we

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2

Mon

Tue

Wed
Home

Thu

Fri

OfficeSat
Restthat the connected antenna a is antenna i, given position x,
can be written as Pr(a = i|x) = Pr(Si (x) ≥ Sj (x) ∀j ∈ X ).
Given a specific realization of Ri we can then writeY  Lj (x)Pr(a = i|x, Ri = r) =Pr Rj
≤ r . (2)Li (x)

Sun
INE

j6=i

Figure 2. Weekly dynamics of the three detected clusters using k-meansclustering: home, office, and the remainder, compared to the independent
statistics of the Instituto Nacional de Estatistica (INE). Using more clusters
yields relatively similar results. The dotted line indicates noon of every day.

first group antennas around the most frequent antennas, basedon Delaunay neighborship. We will only consider groups that
represent more than 5% of the calls. We refer to these groupsof antennas as ‘frequent locations’.
For each frequent location we can identify the weekly
calling pattern. We use this pattern to determine the type oflocation we are dealing with. In particular we perform k-meansclustering, where each cluster represents a type of frequent
location. We find that most of the frequent locations can be
represented by only a few clusters, mainly associated to homeand office, as displayed in Fig. 2. We find excellent agreementwith independent statistics from the Instituto Nacional deEstatistica1 (INE) in terms of time spent at work, as displayedin fig.2. This implies that only the office and the home location
have a clearly identifiable calling pattern, while other frequent
locations cannot be so easily classified.
Since the frequent locations are represented by a multitudeof antennas, we propose a model to estimate a more precise
position (i.e. to determine more precisely the home and office).
We consider a simplified version of the model proposed in [6],also used in [7]. The idea is that users connect to antennas thathave the highest signal strength. This signal strength Si (x) ofantenna i at position x is stochastic, and is given by
Si (x) = pi Li (x)Ri ,(1)

where pi is the power of the antenna, Li (x) is the loss ofthe signal over distance (which decays as a power law withexponent β) and finally Ri represents the stochastic inference
present in the environment (i.e. Rayleigh fading). In particular,
the random variable Ri is an exponentially distributed variablewith mean 1, and independent for all i.
Given a certain location x, we assume the user connects
to the antenna that has the highest total signal strength of allantennas in the neighborhood. We denote by X the set ofantennas and, for a user to connect to an antenna i, requirethat Si (x) ≥ Sj (x) for all j ∈ X . Since Ri is a randomvariable, the signal strength of antenna i is larger than all otherantennas j ∈ X with some probability. Then the probability
1 http://www.ine.pt

Using the law of total probability, we can writeZ ∞Pr(a = i|x) =e−r Pr(a = i|x, Ri = r)dr.

(3)0

In fact, this can be seen as a smooth approximation of theVoronoi tessellation, in which a user will always connect tothe closest antenna.
For each frequent location, we know the number of calls ki
made using antenna i. The probability there were ki calls using
antenna i given position x is then Pr(a = i|x)ki . Hence, thelog likelihood of observing the call frequencies for a certain
location x can then be given asXki log Pr(a = i|x).
(4)log L(x|k) =i∈X

The Maximum Likelihood Estimate (MLE) x̂ of the position
for a frequent location is then given byx̂ = arg max log L(x|k).

(5)x

For finding the MLE, we employ a derivative-free optimization scheme, since the gradient of the likelihood function iscostly to evaluate. In particular, we used the Nelder-Meadalgorithm.
ACKNOWLEDGMENTS
This paper presents research results supported by the Concerted Research Action (ARC) “Large Graphs and Networks”
of the French Community of Belgium and the Belgian Network DYSCO (Dynamical Systems, Control, and Optimization), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy Office.
This work has also been supported by the Orange Labs R&D
Research Grant 46143202. The scientific responsibility rests
with its authors.
R EFERENCES[1] T. de Bailliencourt, T. Beauvisage, F. Granjon and Z. Smoreda, Extended
Sociability and Relational Capital Management: Interweaving ICTs andsocial relations, in Mobile Communication: Bringing Us Together or
Tearing Us Apart?, R. Ling, S. Campbell, eds. Transaction Publishers,
2011.
[2] C. de Kerchove, E. Huens, P. Van Dooren and V. Blondel, Social Leadersin Graphs, in Lecture Notes in Control and Information Sciences, Positive
Systems, Vol.341, pp.231–237, 2006.
[3] Marta C. González, César A. Hidalgo and Albert-László Barabási, Understanding individual human mobility patterns, Nature, Vol.453, pp.779–
782, June 2008.
[4] R. Lambiotte, V.D. Blondel, C. de Kerchove, E. Huens, C. Prieur, Z.
Smoreda, P. Van Dooren, Geographical dispersal of mobile communication networks, Physica A, Vol.387, pp.5317–5325, 2008.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks36

3[5] C. Licoppe, Z. Smoreda, Are social networks technologically embedded?
Social Networks, Vol.27(4), pp.317–335, Oct. 2005.
[6] H. Zang, F. Baccelli, and J. Bolot, Bayesian inference for localizationin cellular networks, Proc 29th Conf Info Comm, pp. 1963–1971, IEEE
Press, 2010.
[7] V.A. Traag, A. Browet, F. Calabrese and F. Morlot Social Event Detectionin Massive Mobile Phone Data Using Probabilistic Location Inference,submitted to IEEE SocialCom’2011.

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37Analysis of Mobile Phone Datasets and Networks, Oct. 10-11, 2011, MIT, Cambridge, MA

Chatty Mobiles:
Individual mobility and communication patterns
Thomas Couronné, Zbigniew Smoreda, Ana-Maria Olteanu
Sociology and Economics of Networks and Services department
Orange Labs R&D, Paris

Introduction

independent mobility records - as in [6]), we will
analyze the relationship between communicationand mobility patterns.

Human mobility analysis is an important issue insocial sciences, and mobility data are among themost sought-after sources of information in urban studies, geography, transportation and territory management. In network sciences mobility
studies have become popular in the past few
years, especially using mobile phone location
data [1,2,3,4,5]. For preserving the customer privacy, datasets furnished by telecom operators areanonymized. At the same time, the large size ofdatasets often makes the task of calculating all
observed trajectories very difficult and time-consuming. One solution could be to sample users.
However, the fact of not having information
about the mobile user makes the sampling delicate. Some researchers [1] select randomly asample of users from their dataset. Others try to
optimize this method, for example, taking into
account only users with a certain number or frequency of locations recorded [2,3]. At the firstglance, the second choice seems to be more efficient: having more individual traces makes theanalysis more precise. However, the most frequently used CDR data (Call Detail Records)
have location generated only at the moment ofcommunication (call, SMS, data connection). Dueto this fact, users’ mobility patterns cannot be
precisely built upon their communication patterns. Hence, these data have evident shortcomings both in terms of spatial and temporalscale.
In this paper we propose to estimate the correlation between the user’s communication and mobility in order to better assess the bias of frequency based sampling. Using technical GSM
network data (including communication but also

Data
One weekday GSM data of the Paris Region territory (12,012 km² - 4,638 sq mi) were used. The
dataset covers 4 million of French mobile phoneusers and more than 94 million records. Data areissued from Orange GSM network probes, they
are anonymous (a secure, random network attributed temporary identity) and contain both cell
localized communication events (calls and SMS)
and mobility events (handover (HO) and location
area update (LAU)).

Figure 1. An example of mobile phone network localization
data types for one user travelling from X to Y

Concerning mobility records, there are two typesof data: HO data are generated during acommunication, when a mobile phone changes
position and is transferred to a new antenna;
LAU records are generated when a devicechanges location area (in Paris Region, a location
area groups on average 150 cells). The LAU is
generated also when a mobile moves from one
location area to the next while not on a call. It is
exactly the information we need for our analysis

1

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38Analysis of Mobile Phone Datasets and Networks, Oct. 10-11, 2011, MIT, Cambridge, MA

as it is independent of the user communication
behavior.
Results
Our analysis was conducted using two separate
record types: communication data and communication-independent itinerancy data. Communication frequency was plotted against the median
number of mobility records (LAU) for each user
(see: figure 2).
Figure 3. Conjoint distribution of users into communicationand itinerancy intensity classes (1-low, 8-high)

The red color signifies the most probable combination and the deep blue color designates theless probable one. As we can observe, redsquares are distributed on the top-right (bothinfrequent communication and mobility), on thebottom-left (both intense communication andmobility) as well as in the center of the matrix(average values). All other combinations, and inparticular high-low combinations, are less frequent. The matrix indicates that in our data therelationship between user mobility and user
communication frequency is really strong.

Figure 2. Median number of local area change as a function ofcommunication frequency

Ninety percent of users have less than 30 communication events (calls or SMS) during the observed day. For this group, we notice a clear,almost linear correlation between the frequency
of communications and the median number oflocation area changes (daily mobility indicator).
The curve reaches a plateau at about 50 communications per day and then the communication mobility link disappears. People who communicate extremely frequently can no longer be distinguished by their median mobility.
The relationship between the number of itinerancy events (LAU) and the median communication frequency has also been studied. Again thiscorrelation is nearly a perfect one: the more mobility records are, the more frequent mobile
communications are.

To complete this approach, the daily displacement distance per user was calculated using all
localized records (calls, SMS, HO, and LAU) andcompared to communication events distribution(see: figure 4).

To analyze conjointly user’s mobility and communication, we constructed 8 equal frequency
ordered classes for each event type (where class 1
is the lowest mobility/communication, and theclass 8 is the most intensive itinerancy/communication). Then we populated an 8x8 matrix withusers having all specific combinations of itinerancy and communication events. The result of
this operation is showed in figure 3.

Figure 4. Median daily distance traveled (km) as a function ofcommunication events frequency

We looked for a best regression model to fit ourdata, where y is the median daily distance in km
and x is the number of communication events(call, SMS). It appears that the best model is aquadratic function:y = – 0.82x² + 7.2x + 20
2

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39Analysis of Mobile Phone Datasets and Networks, Oct. 10-11, 2011, MIT, Cambridge, MA

work, Intelligent Transport Systems, IET 1(1): 15 –26.

This analysis confirms our observation showed infigure 2: the higher the number of communication events, the less strong the increase of themobility distance.
Conclusion
A significant correlation between user mobility
events and communication frequencies confirms
our intuition that in mobile phone usages bothphenomena are interrelated. A highly mobile
person has in fact greater probability to use amobile phone than someone who only commutes
between a few places where s/he can also communicate via a fixed telephone. As his/her correspondents learn with time which is the mostadapted communication channel to reach thisperson, they will also contribute to reinforce theobserved correlation.
From the point of view of human mobility analysis using data from mobile phone, such as CDRs,
our results ask for a very careful examination ofsampling methods which are used. Selecting
users with frequent communication traces, i.e.,with many cell localizations, seems to introduce aclear bias because people having more mobile
communications are also in a more mobile classof the general population.
Definitely, to better calibrate mobile phone data
analysis in this domain a cross-analysis of mobile
phone and survey-like data is needed.
References[1] González MC, Hidalgo CA, Barabási AL, 2008,
Understanding individual human mobility patterns, Nature 453: 779–782.
[2] Song C., Qu Z, Blumm N, Barabási AL, 2010, Limitsof Predictability in Human Mobility. Science 327,
1018 (2010); DOI: 10.1126/science.1177170.
[3] Onnela JP, Arbesman S, González MC, Barabási
AL, Christakis NA, 2011 Geographic constraints onsocial network groups, PLoS ONE 6(4): e16939.
[4] Calabrese F, Di Lorenzo G, Liu L, Ratti C, 2011,
Estimating
Origin-Destination
flows
using
opportunistically collected mobile phone location
data from one million users in Boston Metropolitan
Area, IEEE Pervasive Computing 99, DOI: 10.1109.
[5] Calabrese F, Smoreda Z, Blondel V, Ratti C, 2011,
Interplay between telecommunications and face-toface interactions - a study using mobile phone data,
PLoS ONE 6(7): e208814.
[6] Caceres N, Wideberg J, Benitez F, 2007, Deriving
origin-destination data from a mobile phone net-

3

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Distance Matters: Geo-social Metrics for Mobile
Location-based Social Networks
Salvatore Scellato

Cecilia Mascolo

Computer Laboratory
University of Cambridge

Computer Laboratory
University of Cambridge

ss824@cam.ac.uk

cm542@cam.ac.uk

DatasetBrightkite
Foursquare
Gowalla

ABSTRACTThe widespread adoption of mobile smartphones has led toa significantly large portion of users continuously accessing
online social services in their daily lives. Furthermore, thesedevices offer geolocation capabilities: the ability to shareyour location, to generate location-tagged information andto search for it adds a crucial dimension to these social services. These new features make possible to investigate how
space and geographic distance affect the social connections
created by mobile users on these services.

hki
7.88
22.07
9.48

hCi
0.181
0.191
0.254

hDi
5,651
8,494
5,663

hli
2,041
1,442
1,792

Table 1: Properties of the mobile datasets: numberof nodes N in the social network, average node degree hki, average clustering coefficient hCi, averagegeographic distance between nodes hDi [km], average social link length hli [km].

Here we study the socio-spatial properties of 3 large-scale
online location-based social networks. We observe strong
heterogeneity across users, with different characteristic spatial lengths of interaction across both their social ties andsocial triads. Finally, we describe new metrics able to characterize how geographic distance affects social structure andto capture socio-spatial heterogeneity across mobile users.

1.

N
54,190258,706
122,414tance still matters. In fact, there is universal agreementthat the probability of having a social connection between
two individuals decreases with distance [4, 3, 1].
In this work we study the socio-spatial properties of threedifferent popular mobile LBSNs: Brightkite, Foursquare and
Gowalla. We have collected data about all of them and extracted the social networks among their users. We are able
to assign a “home location” to each user, in order to embed the nodes in a 2-dimensional metric space. We analyze
their global properties, observing robust universal features
across them. Furthermore, we discuss how mobile users areaffected in a heterogeneous way by geographic distance, withsome individuals exhibiting mainly short-range rather thanlong-distance social ties and clusters. Finally, we describe
two geo-social metrics which are able to capture this heterogeneity across social links and social triangles.

INTRODUCTION

Online Location-based Social Networks (LBSNs) have recently attracted millions of users, experiencing a huge popularity increase over a short period of time. Thanks to
the widespread adoption of location-sensing mobile devices,
users can share information about their location with theirfriends. As a consequence, these services offer a groundbreaking opportunity to understand and exploit the spatial
properties of the social networks arising among online users,
but also a potential window on real human socio-spatial behavior.

2.

Spatial networks have been extensively studied, particularly
when dealing with transportation networks, power grids, urban road networks and other systems where nodes are embedded in a metric space [2]. In general, metric distancedirectly influences the network structure of such systems by
imposing higher costs on the connections between distantentities. Social networks, instead, have been largely studied
from a purely topological perspective, focusing on the structural position of their nodes and on structural mechanismsthat describe their evolution. Indeed, the connection costthat heavily affects other types of spatial networks may not
be as important in social systems, particularly when focusing on online interactions. However, some initial results on
the spatial properties of social networks suggest that dis-

MOBILE DATASETS

We study three spatial social networks acquired from different popular mobile online LBSNs. For each service we
extract the social network arising among users and a single
geographic home location for each user, defined as the place
where he/she has more check-ins overall.
Brightkite was founded in 2007 as a social networking website which allows users to share their location with theirfriends: it is available worldwide and it is based on the idea
of making check-ins at places, where users can see who isnearby and who has been there before. Brightkite users canestablish mutual friendship links and they can push their
check-ins to their Twitter and Facebook accounts. We study
a dataset collected in September 2009 which includes thewhole Brightkite user base at that time, with information
about 54,190 users [5].
1

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41

1.0

0.8

0.6

CDF

CDF

0.8

1.0

Brightkite
Foursquare
Gowalla

0.4
0.2
0.0
100

102
103
Distance [km]

104

0.6
0.4
0.2

Friends
Users

101

Brightkite
Foursquare
Gowalla

0.0
100

105

101
102
103
104
Average friend distance [km]

105

(a)
Figure 1: Empirical Cumulative Distribution (CDF)of the geographic distance between all users (dottedline) and between connected friends (solid line) for
the three datasets.

1.0

CDF

0.8

Foursquare was created in 2009 and it has quickly risen as
the most popular location-based service. Users utilize theFoursquare application on their mobile devices, which allows
them to check-in and share with their friends the place where
they are. Many Foursquare users choose to automatically
push their check-in messages to Twitter, which provides a
public API to search and download these messages. Thus,
we have recorded both check-in messages and friendship ties
among Foursquare users on Twitter, where they are publicly
available. Our dataset includes about 250,000 users: weestimate that our sample contains approximately 20% to
25% of the entire Foursquare user base at collection time.

0.6
0.4
0.2
0.0
100

101
102
103
104
Average triangle length [km]

105

(b)
Figure 2: Empirical Cumulative Distribution (CDF)of the average friend distance (a) and average triangle length (b) for each user in the social networks.

Gowalla is a location-based social network created in 2009:its users check-in at places through their mobile devices.
Check-ins are shared with friends: as a consequence, friends
can check where a user is or has been; conversely, it is possible to see all the users that have recently been in a given
place. We have collected a complete snapshot of Gowalla
data in August 2010, including check-ins and social connections, totaling about 120,000 active users.

3.

Brightkite
Foursquare
Gowalla4.

USER PROPERTIES

We now focus on individual users, studying how their social ties stretch across space. We plot the distribution of
the average friend distance of each user in Figure 2(a). The
existence of values over all geographic scales is due to theexistence of users with different characteristic lengths of interaction. For instance, about 10% of users have connections
with an average length of just 10 km, whereas about 20%
of users have average friend distances above 2,000 km. In
particular, since this distribution closely matches the aggregated link length distribution in Figure 1, links with different
geographic lengths do not appear homogeneously across allusers. Instead, there is heterogeneity between users, withsome of them with only short-range connections and otherswith long-distance ties.

GLOBAL PROPERTIES

We first address the spatial properties of the mobile social
networks under analysis, focusing on the main topologicaland geographic measures, reported also in Table 1. The average degree is lower in Brightkite and Gowalla, respectively
7.88 and 9.48, than in Foursquare, where users have on average 22.07 friends. These networks also exhibit high valuesof average clustering coefficient, between 0.18 and 0.26.

We now study the geographic properties of social triangles.
In fact, users tend to belong to several triads, resulting inhigh values of clustering coefficient: our networks exhibit
clustering values between 0.18 and 0.26. To assess whether
geographic heterogeneity arises also for social triangles, we
compute the geographic average triangle length of the threelinks of each triangle and then we compute the average triangle geographic length for each user by considering all thetriangles he/she belongs to. Our aim is to assess the geographic span of a user’s social triangles, whatever their number might be. In Figure 2(b) we show the distribution of theaverage triangle length for each user: triangles with different
geographic span are not equally arising among all users, butinstead there are users with smaller triads and users withwider ones. For example, there are at least 20% of users
with an average triangle length less than 100 km, while the
top 20% have values above 2,000 km.

The average geographic distance between users hDi is consistently larger than the average distance between friends hli
across all the datasets: while the first value ranges between
5,600 and 8,500 km, the latter has much shorter values, between 1,400 and 2,000 km. This already provides evidence
that the probability of having a social link between two users
decreases with distance: we will further investigate this relationship later. The distribution of social link length iscomparable across the three datasets, as shown in Figure 1:
about 40%-50% of all couples of friends are within 100 km,
with more than 3% of all links being shorter than 1 km. Instead, the distribution of distances among users, also shownin Figure 1, has a different behavior: about 50% of users areat distances larger than 4,000 km across all the networks.
2

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42

1.0

CDF

0.8

of node locality for the three networks are shown in Figure 3(a): node locality is able to capture how different users
have heterogeneous spatial properties, with values spanningthe entire range. For example, in Brightkite and Gowalla
about 40% of users have a node locality higher than 0.90,
whereas in the FourSquare dataset this phenomenon is moreevident, with 70% of users above 0.90.

Brightkite
Foursquare
Gowalla

0.6
0.4
0.2
0.0
0.0

0.2

0.4
0.6
Node locality

0.8

1.0

Similarly, we define the geographic clustering coefficient as
an extension of the clustering coefficient used for complex
networks. The clustering coefficient measures the proportion of triangles among the neighbors of a given node: thegeographic clustering coefficient of node i is thus defined in
the same way as the clustering coefficient, but each existing
triangle between nodes i, j and k is assigned a weight wijk
defined as:

(a)
1.0

CDF

0.8

Brightkite
Foursquare
Gowalla0.6
0.4

wijk = e
0.2
0.0
10−3

10−2
10−1
Geographic clustering coefficient

∆ijk
β

(2)

where ∆ijk is the maximum length among the three links,
that is ∆ijk = max(lij , lik , ljk ). We define wijk = 0 if there
is no link between j and k. Since this measure uses the
maximum weight among all the links of a triangle, it focuseson nodes which are all close to each other: when just oneof the three nodes is not close to the other two, the weight
will immediately decrease. This emphasizes social triangles
where users are extremely close to each other. Again, theparameter β is used to scale the values of the measure.

100

(b)
Figure 3: Empirical Cumulative Distribution (CDF)
of node locality (a) and geographic clustering coefficient (b) for each user in the social networks.

5.

−

The three networks exhibit different values of geographic
clustering coefficient: while Brightkite has an average valueof 0.165 and Gowalla of 0.171, FourSquare scores a muchhigher 0.209. The geographic clustering coefficient is close
to the standard clustering coefficient, signaling how triangles tend to form at shorter distances. The probability distributions of the geographic clustering coefficient are shownin Figure 3(b): again, the three networks exhibit a nonnegligible portion of users with a coefficient close to 1.

GEO-SOCIAL MEASURES

Finally, we define two new geo-social network measures which
are able to capture, respectively, the geographic heterogeneity we have observed in social ties and social triangles. We
represent a spatial social network as a graph G with N nodes
and K links: nodes represent users and a link among two
nodes exists if there is a social tie between them (e.g., a person lists another user as one of his/her friends). Nodes areembedded in a 2-dimensional metric space where the distance between two nodes i and j is given by the geographic
distance Dij between their locations on the planet. Thisdistance is used as the length of the link lij between nodes
i and j.

6.

CONCLUSION

In this work we have studied the socio-spatial propertiesof users of mobile location-based services. We observe anddiscuss heterogeneity in user socio-spatial behavior: users
exhibit friendship connections across a wide range of geographic distance, showing similar variability in the social
triads they belong to. Finally, we have defined and discussed two novel geo-social measures that capture such user
heterogeneity.

Then, we define a metric to capture the geographic closeness of the neighbors of a certain node. Let us consider anundirected geographic social network, a node i with a particular geographic position and the set Γi of its neighbors.
The node degree ki is the number of these neighbors, thatis ki = |Γi |. Then, the node locality of i can be defined as ameasure of how much geographically close its neighbors are
and it is computed as follows:
1 X −lij /βe(1)
N Li =
ki j∈Γ

7.

REFERENCES[1] L. Backstrom, E. Sun, and C. Marlow. Find me if you
can: improving geographical prediction with social andspatial proximity. In Proceedings of WWW ’10, pages
61–70, 2010.
[2] M. Barthélemy. Spatial Networks. Physics Reports,
499:1–101, 2011.
[3] R. Lambiotte, V. Blondel, C. Dekerchove, E. Huens,
C. Prieur, Z. Smoreda, and P. Vandooren. Geographicaldispersal of mobile communication networks. PhysicaA, 387(21):5317–5325, September 2008.
[4] D. Liben-Nowell, J. Novak, R. Kumar, P. Raghavan,and A. Tomkins. Geographic routing in social networks.
PNAS, 102(33):11623–11628, August 2005.
[5] S. Scellato, C. Mascolo, M. Musolesi, and V. Latora.
Distance Matters: Geo-social Metrics for Online Social
Networks. In Proceedings of WOSN’10, June 2010.

i

where β is a scaling factor which avoids extremely smallvalues of node locality when links have large lengths. By
definition, N Li is always normalized between 0 and 1. The
value of β can be chosen so that networks with different
geographic size can still be compared with each other.
Users exhibit an overall high average node locality: Brightkite
has an average value of 0.82 and Gowalla of 0.85, while in
FourSquare this value goes up to 0.90. The distributions
3

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

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A tale of many cities: universal patterns in human urban mobility
Anastasios Noulas
Computer Laboratory
University of Cambridge

Salvatore Scellato
Computer Laboratory
University of Cambridge

Renaud Lambiotte
Department of Mathematics
University of Namur

an346@cl.cam.ac.uk

ss824@cl.cam.ac.uk

renaud.lambiotte@fundp.ac.be

Massimiliano Pontil
Computer Science Department
University College London

Cecilia Mascolo
Computer Laboratory
University of Cambridgem.pontil@cs.ucl.ac.uk

cm542@cl.cam.ac.uk

July 23, 2011
”gravity-models” have a long tradition in quantitative geography and urban planning and have beenused to model a wide variety of social systems, e.g.
human migration [11] and traffic flows [12]. The
second camp argues instead that there is no direct relation between mobility and distance, andthat distance is a surrogate for the effect of intervening opportunities [13]. The migration fromorigin to destination is assumed to depend on thenumber of opportunities closer than this destination. A person thus tends to search for destinations where to satisfy the needs giving rise to its
journey, and the absolute value of their distance isirrelevant. Only their ranking matters. Displacements are thus driven by the spatial distribution ofplaces of interest, and thus by the response to opportunities rather than by transport impedance asin gravity models. The first camp appears to havebeen favoured by practitioners on the grounds ofcomputational ease [14], despite the fact that several statistical studies have shown that the concept
of intervening opportunities is better at explaining
a broad range of mobility data [15, 16, 17, 18, 19].
This long-standing debate is of particular interest in view of the recent revival of empirical research on human mobility. Contrary to traditional
works, where researchers have relied on surveys,

Since the seminal works of Ravenstein [1], themovement of people in space has been an active
subject of research in the social and geographicalsciences. It has been shown in almost every quantitative study and described in a broad range ofmodels that a close relationship exists between mobility and distance. People do not move randomly
in space, as we know from our daily lives. Humanmovements exhibit instead high levels of regularity and tend to be hindered by geographical distance. The origin of this dependence of mobility on
distance, and the formulation of quantitative laws
explaining human mobility remains, however, an
open question, whose answer would lead to many
applications [2], e.g. improve engineered systems
such as cloud computing and location-based recommendations [3, 4], and yield insight into a variety ofimportant societal issues, such as urban planningand epidemiology [5, 6, 7].
In classical studies, two related but diverging
viewpoints have emerged. The first camp argues
that mobility is directly deterred by the costs (intime and energy) associated to physical distance.
Inspired by Newton’s law of gravity, the flow ofindividuals is predicted to decrease with the physical distance between two locations, typically as a
power-law of distance [8, 9, 10]. These so-called
1

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small-scale observations or aggregate data, recent
research has taken advantage of the advent of pervasive technologies in order to uncover trajectoriesof millions of individuals with unprecedented resolution and to search for universal mobility patterns,
such to feed quantitative modelling. Interestingly,
those works have all focused on the probabilistic
nature of movements in terms of physical distance.
As for gravity models, this viewpoint finds its roots
in Physics, in the theory of anomalous diffusion. It
tends to concentrate on the distributions of displacements as a function of geographic distance.
Recent studies suggest the existence of a universal
power-law distribution P (∆r) ∼ ∆r−β , observedfor instance in cell tower data of humans carrying
mobile phones β = 1.75 [21] or in the movements of”Where is George” dollar bills β = 1.59 [22]. This
universality is, however, in contradiction with observations that displacements strongly depend on
where they take place. For instance, a study ofhundreds of thousands of cell phones in Los Angeles
and New York demonstrate different characteristic
trip lengths in the two cities [20]. This observationsuggest either the absence of universal patterns inhuman mobility or the fact that physical distanceis not a proper variable to express it.
In this work, we address this problem by focusing on human mobility patterns in a large number of cities across the world. More precisely, we
aim at answering the following question: ”Do people move in a substantial different way in different
cities or, rather, movements exhibit universal traits
across disparate urban centers?”. To do so, we takeadvantage of the advent of mobile location-basedsocial services accessed via GPS-enabled smartphones, for which fine granularity data about human movements is becoming available. Moreover,
the worldwide adoption of these tools implies thatthe scale of the datasets is planetary. Exploiting data collected from public check-ins made byusers of the most popular location-based social
network, Foursquare, we study the movements ofabout 800,000 users around the globe over a period of about six months, and study the movements
across more than 5 million places in metropolitan
cities that span five continents.

After discussing how at larger distances we areable to reproduce previous results of [21] and [22],we also offer new insights on some of the important questions about human urban mobility acrossa variety of cities. We first confirm that mobility, when measured as a function of distance, does
not exhibit universal patterns. The striking element of our analysis is that we observe a universal behavior in all cities when measured with theright variable. We discover that the probability oftransiting from one place to another is inversely
proportional to a power of their rank, that is, thenumber of intervening opportunities between them.
This universality is remarkable as it is observed despite cultural, organizational and national differences. This finding comes into agreement with thesocial networking parallel which suggests that theprobability of a friendship between two individuals
is inversely proportional to the number of friends
between them [23], and only indirectly on physical distance. More importantly, our analysis is infavour of the concept of intervening opportunities
rather than gravity models, thus suggesting thattrip making is not explicitly dependent on physical distance but on the accessibility of objectives
satisfying the objective of the trip. Individuals
thus differ from random walkers randomly exploring physical space because of the motives driving
their mobility.
Our results are confirmed with a series of simulations verifying the hypothesis that the density
is the driving force of urban movement. By using
only information about the distribution of placesof a city as input and by coupling our model witha rank-based mobility preference we are able to reproduce the actual distribution of movements observed in real data.

References[1] E.G. Ravenstein (1885) The Laws of Migration,
Journal of the Royal Statistical Society 48,167227.
[2] P. Hui and J. Crowcroft (2008) Human Mobility
Models and Opportunistic Communication System

2

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks45[15] E. Miller (1972) A note on the role of distancein migration: costs of mobility versus intervening
opportunities, J. Reg. Sci. 12, 475478.

Design, Royal Society Philosophical Transactions B
366, 2005-2016[3] D. Quercia, N. Lathia, F. Calabrese, G. Di
Lorenzo and J. Crowcroft (2010) Recommending Social Events from Mobile Phone Location Data, Proceedings of IEEE ICDM 2010[16] K.E. Haynes, D. Poston and P. Sehnirring (1973)
Inter-metropolitan migration in high and low opportunity areas: indirect tests of the distance and intervening opportunities hypotheses, Econ. Geogr. 49,
68-73.

[4] V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang
(2010) Collaborative location and activity recommendations with GPS history data, WWW 10, 10291038.

[17] W.J. Wadycki (1975) Stouffer’s Model of Migration: A Comparison of Interstate and Metropolitan
Flows, em Demography 12, 121-128.

[5] K. Nicholson and R.G. Webster, Textbook of Influenza (Blackwell, Malden, Massachusetts, 1998)[18] R.H. Freymeyer and P.N. Ritchey (1985) Spatial
Distribution of Opportunities and Magnitude of Migration: An Investigation of Stouffer’s Theory, Sociological Perspectives 28, 419-440.

[6] L. Hufnagel, D. Brockmann and T. Geisel (2004)
Forecast and control of epidemics in a globalized
world, Proc Natl Acad Sci USA 101, 15124.

[19] C. Cheung and J. Black (2005) Residential
location-specific travel preferences in an intervening
opportunities model: transport assessment for urban
release areas, Journal of the Eastern Asia Society forTransportation Studies 6, 3773-3788.

[7] V. Colizza, A. Barrat, M. Barthlemy and A. Vespignani (2007) Predictability and epidemic pathways inglobal outbreaks of infectious diseases: The SARS
case study, BMC Med. 5, 34.

[20] S. Isaacman, R. Becker, R. Cceres, S. Kobourov,
J. Rowland, A. Varshavsky (2010) A Tale of Two
Cities, 11th Workshop on Mobile Computing Systems and Applications[8] V. Carrothers (1956) A Historical Review of theGravity and Potential Concepts of Human Interaction, J. of the Am. Inst. of Planners 22, pp. 94-102.
[9] A.G. Wilson (1967) A statistical theory of spatial
distribution models, Transportation Research 1, pp.
253-269. 54, pp. 68-78[21] M. C. Gonzlez, C. A. Hidalgo and A.-L. Barabsi(2008) Understanding individual human mobility
patterns, Nature 453, 779-782. D. Brockmann and
F. Theis (2008) Money Circulation, Trackable Items,
and the Emergence of Universal Human Mobility
Patterns, IEEE Pervasive Computing 7, 28 - 35

[10] S. Erlander and N. F. Stewart (1990) The Gravity Model in Transportation Analysis: Theory andExtensions, Brill Academic Publishers, Utrecht.

[22] D. Brockmann, L. Hufnagel and T. Geisel (2006)
The scaling laws of human travel, Nature 439, 462465.

[11] M. Levy (2010) Scale-free human migration andthe geography of social networks, Physica A 389,
4913-4917.

[23] D. Liben-Nowell, J. Novak, R. Kumar, P. Raghavan and A. Tomkins (2005) Geographic routing insocial networks, PNAS 102,11623-11628.

[12] W.-S. Jung, F. Wang and H.E. Stanley (2008)
Gravity model in the Korean highway, Europhys.
Lett. 81, 48005.
[13] S. Stouffer (1940) Intervening opportunities: A
theory relating mobility and distance, American Sociological Review 5, 845-867[14] S.M. Easa (1993) Urban Trip Distribution in Practice. I: Conventional Analysis, Journal of Transportation Engineering 119, 793-815

3NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

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1

Finding Meaningful Usage Clusters From
Anonymized Mobile Call Detail Records
Richard A. Becker, Ramon Caceres, Chao Han*, Karrie Hanson, Ji Meng Loh,
Simon Urbanek, Alexander Varshavsky and Chris Volinsky
AT&T Labs - Research (* Virginia Tech University)

A. Introduction
Given the ubiquity of cell phones and their frequent use, cell
phone calling patterns can be used to study the location andmovements of large numbers of people for cellular network
operation, urban planning and event management. In thisabstract, we investigate ways to cluster mobile phone usage
signatures derived from Call Detail Records (CDRs) in order
to identify common usage patterns. Specifically, we apply
unsupervised clustering algorithms to summaries derived fromanonymized CDRs to identify groups of users who share thesame patterns of calling and texting intensity. Typical behaviorof a cluster is represented by the mean behavior in that group.
The clustering result can also be combined with cell antenna
location to better understand the mobility patterns for urban
planning. Due to the lack of space, we only summarize our
key contributions here. Please see our full paper [2] for details.
B. Data and Methods
We analyze anonymized CDRs collected over a 60 day
period (November 29, 2009 to January 27, 2010) from 35
cell towers located within 5 miles of the center of a suburban
city in the Northeast United States with approximately 20,000
residents. The CDRs include 475,000 unique handsets thatgenerated around 15 million voice calls and 26 million text
messages. We studied these cell phone records to group citydwellers into categories useful to urban planners. Each CDR
records the starting time and duration of a voice call or Short
Messaging Service (SMS) transaction, and the locations andazimuths of the cell tower antennas associated with the event.
Given the sensitivity of CDR data, we took several steps
to ensure the privacy of individuals. First, only anonymousrecords were used in this study. The data was collected andanonymized by a party not involved in the data analysis.
Personally identifying characteristics were removed from ourCDRs. CDRs for the same phone are linked using an anonymous unique identifier, rather than a telephone number. No
demographic data is linked to any cellphone user or CDR.
Second, all our results are presented as aggregates. That is, no
individual anonymous identifier was singled out for the study.
Finally, each CDR only included location information for thecellular towers with which a phone was associated during acall or text message. The phones were effectively invisible tous aside from these events.
Our interest is in cell phone usage by time of day andday of week, so we preprocess the CDRs for each user by

aggregating the calls and text messages into separate bins.
Each bin represents a particular hour of the day and day of
the week, resulting in 24 × 7 = 168 bins. In order to makevoice minutes and SMS counts comparable, we normalize
SMS usage so that both groups have the same global mean,
making one SMS message correspond to 1.5 minutes of voice
calling. The final step of the preprocessing concatenates thevoice and SMS usage bins for each user to form a vector of
336 elements, which is normalized to sum to 1. Thus the data
for each individual is the relative fraction of all calling madeby voice or SMS in each hour of each day of the week.
We are now ready to cluster, but there are many clustering
algorithms we might use, each with parameters to set or
estimate. For example, in K-means clustering, we need tosupply a value for k, the desired number of clusters. In
addition, many users in our dataset have very little activity; forexample, the median number of SMS events per user per day
is 2. We anticipate that it may be difficult to cluster them, so it
will be important to have techniques that allow us to evaluate
the performance of the algorithms.
In order to assess whether we can cluster low usage users
well, we rank users by their usage volume and then split
the users into 10 slices, with around 15,000 users in eachslice. We perform clustering algorithms on the slices one by
one and observe how clustering results change. The clustering
performance is evaluated by two fold cross-validation which
can also help investigate the prediction accuracy for new data.
Particularly, within each slice, we randomly sample 50% of thedata to be used as training set for clustering, and use the rest
to test the result. Since we do not have any prior knowledge
about the user classification, there is no group label available
as in usual cross-validation approach. Thus for each user inthe test set, instead of aggregating the usage vector over theentire two months, we aggregate the activities over December
and January separately such that every test set user has two
usage vectors. Each vector is assigned to a cluster according
to its proximity to the cluster centers derived from clustering
the training set. If we assume that the usage pattern for eachuser is consistent over time, the cluster assignment for both
vectors should match. The vector agreement is measured by
Cohen’s Kappa index [3], which evaluates the concordance
using relative observed agreement adjusted by the hypotheticalprobability of agreement by chance. We perform the random
sampling and the cross-validation three times for all slices andget similar agreement index values across the three runs, whichindicates that the consistent usage assumption is valid.

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We have tried several widely used clustering algorithms
which include: 1. K-means which is an iterative procedurethat refines initial estimates of the k cluster centroids into
final centroids in an attempt to minimize the sums of squaresof distances from each point to the nearest cluster centroid. 2.
Hierarchical clustering (with ‘average’, ‘complete’ and ‘ward’
agglomeration methods) which starts with every observation
data in its own cluster and then repeatedly merges the closest
pair of clusters until all of the data are in one cluster. 3.
Partition Around Medoids (PAM) [5] which is similar to Kmeans but takes medoid as the representative point such thatthe algorithm is robust to outliers. 4. Fuzzy C-Means [4] whichallows each data point to belong to more than one clusterwith different membership degrees (between 0 and 1), where
lower membership values may indicate data noisiness. However, Fuzzy clustering usually takes much longer time than
hard clustering methods such as K-means. For the clustering
methods which are sensitive to initialization (e.g., K-means,
PAM, Fuzzy), we run the algorithms with different starting
points and pick the solution that provides the best Calinski
index [7]. The Calinski index measures the dispersion of thedata points within a cluster and between the clusters by taking
the ratio of the between cluster sum of squares and withincluster sum of squares (adjusted by degrees of freedom). A
higher Calinski index indicates better cluster separation.
We refer to the Gap Statistic [9] and the Silhouette Statistic[5] for determining the number of clusters k, because theclustering literature shows they have good performance andbecause they are applicable to any clustering technique anddistance metric. The Gap Statistic compares the change in
Wk (the pooled within-cluster dispersion) as k increases forthe original data with that expected for data generated from areference null (uniform) distribution. The optimal number ofclusters is estimated as the value of k for which log(Wk ) falls
the farthest below its expected curve. The Silhouette width
for each point compares the average distance of a point to allother points in nearest cluster besides its own cluster with theaverage distance of the point to all other points in the samecluster. Observations with a large Silhouette width are wellclustered. We can make the judgment about k by checking
the Silhouette plot visually which shows the within-clustercompactness and between-cluster separation.
If we take Euclidean distance as the distance metric, wefind that K-means has a better performance than the otheralgorithms in the top 7 slices (top 70% quantiles ordered byusage). For the low volume users in the bottom 3 slices (lessthan 30 minutes of calls/SMS over the 60 day period), theclustering approaches all perform poorly and generate similar
Kappa values around 0. Since low volume users generatevery few calls or text messages, their usage vectors are quite
sparse, typically consisting of isolated spikes. Regardless ofthe number of clusters k, the conventional clustering scheme
based on Euclidean distance always results in one large cluster
and many small clusters consisting entirely of individual binsfrom the distribution. The location of the bins is random,based solely on the initial cluster seeds. The reason for suchodd clusters is that the Euclidean metric does not take into
account any additional topology of the bins; also, in a high-dimensional space, all sparse vectors are roughly equidistantfrom each other.
To find meaningful clustering structure for the low volumeusers, we selected a different distance metric: the EarthMover’s Distance (EMD) [8], which can incorporate the notionof nearness between bins naturally. EMD is an intuitive metricbetween distributions if we think of them as piles of sandsitting on the ground (underlying domain). Each pile of sandis an observed sample. To quantify the difference between
two distributions, we measure how much sand must be moved
to turn one distribution into the other. EMD is the minimal
total ground distance travelled weighted by the amount of sandmoved. A major advantage of using EMD is that it allows us todefine a topology on the bins. Since the boundaries of bins byhour are in a sense arbitrary, we would consider neighboringbins in time as ‘close’. Also human activities typically follow
a daily cycle, so that there is a similarity between adjacentdays of the week at the same time. Finally, we have two typesof activities, voice and SMS, which are distinct, so we model
them as being at a constant distance from each other. EMD
allows us to specify this complex relationship between thebins. The biggest drawback of using EMD for clustering is its
high computational complexity (runs in O(n3 log(n)) time).
When dealing with large datasets as in our case, the exact
computation of EMD becomes impractical. To alleviate thisproblem, we use a modified version [1] which reduces thecost to linear time. We applied hierarchical clustering with
EMD to the bottom 30% of the usage distribution. Even withvery sparse vectors, EMD is capable of separating users into
coherent groups.
C. Results
We illustrate how to cluster data from CDRs to arrive atmeaningful groups by applying K-means to a subset of 30,000
high-volume users (at least 7.5 hours of combined activity overthe 60 day period). Plots of the Gap and Silhouette statistics
confirmed that the optimal number of clusters was in the rangeof 5 to 7. The Kappa value generated by K-means with 7
clusters. achieves 0.6 (which indicates substantial agreement[6]) for the top 20% of the data. The resulting clusters (Figure1) are distinct and informative. For example, cluster 1 consistsof voice call activity just before and after work hours; cluster 2
consists of heavy voice users during weekday business hours;
cluster 3 is primarily weekday usage of both voice and SMS;
cluster 4 is voice calling, primarily in the evening; cluster 6 isafter-work voice calling; cluster 7 is a business-hour texting.
Let’s explore cluster 5, the largest cluster, representing
22% of the 30K clustered users, or about 6600 users. We
hypothesize that cluster 5 consists of students because of
the heavy SMS use on weekdays (3pm-10pm) and the early
beginning and late ending on weekends (10am-1am). We thenattempt to see if locations of use, represented approximately
by antennas, can help confirm the hypothesis. Figure 2 shows
the activities in each of the clusters on the antenna that points
in the direction of the city high school. The figure shows amatrix of ‘lip plots’, where the two rows represent weekends
and weekdays, and the columns represent the different clusters.

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Fig. 1. Heat maps of the seven cluster means, each representing a specific usage profile. Each profile consists of the voice usage matrix (left of the graystrip) and the SMS usage matrix (right of the grey strip) across hour of the day (24 rows) and day of the week (7 columns). Darker colors indicate higher
usage. The blue bar at the top denotes the relative size of the cluster.

the sparsely populated usage vectors of low volume users. For
these, we chose hierarchical clustering with EMD to take into
account the underlying topology of the distributions.
R EFERENCES[1] D. Applegate, T. Dasu, S. Krishnan, and S. Urbanek. Unsupervised
clustering of multidimensional distributions using earth mover distance.
In KDD, San Diego, CA, 2011.
[2] R. Becker, R. Cáceres, K. Hanson, J. M. Loh, S. Urbanek, A. Varshavsky,
and C. Volinsky. Clustering anonymized mobile call detail records tofind usage groups. In 1st Workshop on Pervasive Urban Applications(PURBA), 2011.
[3] J. Cohen. A coefficient of agreement for nominal scales. Educationaland Psychological Measurement, 20:38–46, 1999.
[4] J. C. Dunn. A Fuzzy Relative of the ISODATA Process and Its Use in
Detecting Compact Well-Separated Clusters, 1973.
[5] L. Kaufman and P. J. Rousseeuw. Finding Groups in Data. New York,1990.
[6] J. Landis and G. Koch. The measurement of observer agreement for
categorical data. Biometrics, 33:159—-174, 1977.
[7] Milligan and Cooper. An examination of procedures for determining thenumber of clusters in a data set. Psychometrika, 50:159—-179, 1985.
[8] Y. Rubner, C. Tomasi, and L. J. Guibas. A metric for distributions
with applications to image databases. In Proceeding of International
Conference On Computer Vision, pages 59––66, 1998.
[9] R. Tibshirani, G. Walther, and T. Hastie. Estimating the number of dataclusters via the gap statistic. Journal of the Royal Statistical Society B,
63:411–423, 2001.

Fig. 2. Lip plot of activities in each of the clusters on the high school direction
antenna. Lines show the average volume of voice (red) and SMS (blue). The
plot for cluster 5 on weekdays stands out with spikes at 7:30am, 11:30am
and 2:30pm, which match the times of school start, lunch, and dismissal.

Each plot shows the average volume of voice (red) and SMS(blue, plotted below the y-axis) from 6AM to 6PM during
our study period. Cluster 5 stands out because of activity
spikes that appear linked to the school day; the spikes occur
weekdays at 7:30AM when school begins, at 11:30AM during
an open lunch period, and at 2:30PM dismissal. Usage for allother clusters show smooth changes over time and no similar
spikes, indicating that students are not in the other clusters. In asimilar manner, we investigated cluster 1, which we tentatively
identified as commuters driving to and from work; it turned
out to have a much greater proportion of calls involving amoving cell phone (connecting to more than 5 different cell
towers).
D. Conclusions
Understanding cell phone usage patterns is an important step
towards creating applications and services useful for urban
communities. Our study involved clustering of usage patterns
found in call detail records gathered in a small city. We
analyzed anonymized versions of these records, ran a seriesof clustering algorithms to find phones that exhibited similar
usage patterns, and relied on cross-validation to check theclustering performances. We found that for relatively highvolume users, K-means with Euclidean distance provides aninformative grouping. However, K-means failed in clustering

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49

1

Using Cellular Network Data for Urban Planning

In this paper, we demonstrate that cellular network data canbe used to determine the geographical distributions of homelocations of workers (laborshed) and partiers (partyshed) in acity and validate our methodology by comparing our results
to the 2000 Census. We also present a novel visualization
technique, called lip plots, and show how it can be used to

nh
a

●

Ma

With the continuing urbanization of the world’s populationand the rapid growth of cities, urban planners are facedwith many challenges, including heavily congested roads,
overzealous development, and increasing pollution. To efficiently address these problems, urban planners need to develop
a better understanding of the dynamics of modern cities. In thispaper, we explore the use of anonymized Call Detail Records(CDRs) to capture city dynamics.
To conduct our study, we collected anonymized CDRs fromthe cellular network of a large US communications service
provider. We captured transactions carried by the 35 cell
towers (roughly 300 antennas) located within 5 miles of thecenter of Morristown, NJ, a suburban city in the greater New
York City metropolitan area. In place of the phone number ofthe person involved in a transaction, each CDR was assigned
an anonymous identifier consisting of the 5-digit billing zip
code and a unique integer. Each CDR also contains the startingtime of the voice or SMS event, the duration of the event,and the locations and azimuths of the cell tower antennas
associated with the event. We collected voice and SMS traffic
for 60 days between November 29, 2009 and January 27, 2010.
In total, we collected 15 million voice CDRs and 26 million
SMS CDRs for 475,000 unique phones.
Given the sensitivity of the data, we took several steps to ensure the privacy of individuals. First, only anonymous records
were used in this study. In particular, personally identifying
characteristics were removed from our CDRs. CDRs for thesame phone are linked using an anonymous unique identifier,rather than a telephone number. No demographic data is linked
to any cellphone user or CDR. Second, all our results arepresented as aggregates. That is, no individual anonymous
identifier was singled out for the study. By observing andreporting only on the aggregates, we protect the privacy of
individuals. Finally, each CDR only included location information for the cellular towers with which a phone was associatedduring a voice call or at the time of a text message. The phones
were effectively invisible to us aside from these events, and
we only knew those phone locations at the granularity of anantennas’s coverage area, often greater than one square mile.

ttanRichard A. Becker, Ramon Caceres, Karrie Hanson, Ji Meng Loh,
Simon Urbanek, Alexander Varshavsky and Chris Volinsky
AT&T Labs - Research{rab, ramon, karrie, loh, urbanek, varshavsky, volinsky} @research.att.com

New York

New Jersey

Fig. 1. Morristown laborshed maps calculated from CDR data. The red dot
represent the center of Morristown.

capture the ‘life beat’ of a city. Due to the lack of space, weonly summarize our key contributions below. Please see ourfull paper [1] for details.
Calculating and Validating the Laborshed
Morristown is a regional center of commerce and shopping,with a developed downtown area, many office complexes, alarge hospital, and the county courthouse. It draws a large
worker base from the nearby suburbs and even some from themuch larger New York City. The geographical area representing where a city’s workers live is known as its laborshed.
We used our CDR data to calculate the laborshed forMorristown. We classified a cellphone user as a worker if she
is frequently observed in Morristown during business hours(9am to 5pm, Monday to Friday). More specifically, a worker
needs to satisfy two conditions. First, she needs to engage in an
average of at least 4 calls/messages per week during businesshours, involving one of the Morristown cell towers. Second,
she needs to make those calls/messages on an average of atleast 2 unique weekdays per week. We derived these thresholds
experimentally and observed that moderate changes to thesevalues did not affect our results. We used account billing ZIP
codes to identify place of residence.
Figure 1 shows a contour map of the Morristown laborshed
as calculated from the CDR data. Studying the map reveals ahigh-density region centered directly over Morristown, indicating a large concentration of people who both live and work in

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Census estimatesFig. 2.
Scatterplot showing agreement between Morristown laborshed
numbers from CDR data and US Census data. Each point represents one ZIP
code. The solid line shows the best linear fit, where the CDR count equals
0.387 of the Census count. If the CDR estimates exactly matched the Census
numbers, the points would fall on the dotted line.

Morristown. We also see that many more workers come fromareas north of Morristown than south, and that there seems
to be a cluster of workers to the east, in the more heavily
populated areas close to New York City. Additionally, there
are some pockets of workers who come from towns west ofMorristown.
We validate our CDR-based laborshed results by comparing
them to publicly available US Census data. We used the 2000
Census Transportation Planning Package (CTPP), specificallythe "Journey to Work" package which includes detailed information on commuting patterns including counts of commuters
to and from specific census tracts [2]. We mapped census tracts
to ZIP codes by calculating the proportion of a census tractthat fell within each ZIP code of interest.
Figure 2 compares our laborshed estimates against thecorresponding Census CTPP data. We draw each ZIP code
as a point reflecting the relationship between our estimateswith the Census numbers. We do not expect our estimatesto have perfect agreement with the Census numbers because
our CDR records only show those who are actively generating
calls or SMS records, and reflect only the activity of the partof the population on our company’s network. Additionally, thecensus data we had access to is a decade old, and commuting
patterns might have changed significantly in that time. Still we
do expect our points to align with theirs up to a multiplicative
factor and hence should fall close to a straight line in this plot.
For comparison, we show the y = x equality line as a dotted
line, and the best linear fit, y = .387x as a solid line. The
correlation coefficient is 0.81. There does seem to be a clear
correspondence between the CDR- and census-based numbers,
and if we want to roughly estimate numbers of people, wewould multiply the CDR numbers by 1/.387.
To summarize, we argued that understanding where workers

Fig. 3. Morristown partyshed map showing the home locations of people who
used their cellphones during weekend late nights in downtown Morristown.
Comparing to the earlier laborshed maps, partiers’ homes are concentrated inareas closer to Morristown than workers’ homes.

live is important for urban planning and showed that thelaborshed generated from CDRs matches closely that obtained
from census data. These results give confidence in the validityof our approach. Our approach has significant advantages overthe census because its low cost makes it practical to generatelaborshed results much more frequently, for example every few
months instead of every ten years.
Calculating the Partyshed
Similarly, we can apply these techniques to other groups ofpeople besides daytime workers. Like many cities, Morristown
has a lively bar and restaurant scene that attracts people
from other communities as well as locals. We refer to thegeographical distribution of where this group lives as thepartyshed. We look for cellphone users who have voice callor text messaging activity on late weekend nights (10pm to
3am, Fridays to Sundays). The resulting partyshed is shownin Figure 3. It appears that the distribution of partiers is
considerably more concentrated in and near Morristown thanthe distribution of workers. Nonetheless, there is still some
representation of people who live far away, even as far as New
York City. Knowing where groups of revelers come from andwhere they return at the end of the night could allow towns
to tailor services such as late-night shuttle buses intended tokeep inebriated drivers off the road.
Capturing the Lifebeat of a City
In this section, we demonstrate that it is possible to identify
patterns of human activity in different parts of a city byobserving cell phone usage in different cell tower antenna
coverage regions. We refer to these patterns as the lifebeatof a city.
To analyze data from multiple cell tower antennas simultaneously, we developed a novel visual display capable ofrepresenting the multivariate nature of the data. We first

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3

6am noonnoon 6pm

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6am

Voice in
Voice out
SMS inSMS out

Sat(a) Downtown Antenna

Tue(b) High School Antenna

Fig. 4. Lip plots of voice call and SMS volumes show unusual spikes highlighting local patterns or events in Morristown. Call volume (plotted upwards;
inbound: red; outbound: blue) and SMS volume (plotted downwards; inbound: light green; outbound: dark green) on two antennas are shown. The antenna in(a) points towards the commercial and restaurant district and the antenna in (b) points towards the high school. A voice peak occurs Saturday at 2AM when
the bars close. Both voice and SMS peaks occur Tuesday when the school lets out.

aggregated the underlying data in two-minute intervals andthen aggregated by the day of the week, which allows the studyof day-specific patterns. In total, we ended up with 720 twominute bins for each day of the week for both voice calls and
SMS messages. We then used the principle of small multiples
to display the data for all combinations of the partitioned variables. (a high-resolution, zoomable version of this complete
display is available at http://bit.ly/BigLipPlot).
Here we present plots for two specific antennas in Morristown in detail. Figure 4 shows usage plots captured on
two different days of the week for two antennas locatedon the same cell tower but pointing in different directions.
The x-axis represents time, starting and ending at 6am. The
height of the plot shows the amount of traffic: height above
the axis represents voice call volume, while height below
the axis represents SMS volume. By using these opposite
directions of the axes we avoid overplotting and retain theability to recognize shapes at a glance. In addition, our visual
cognitive system is good at evaluating symmetry quickly, thus
we can quickly assess whether voice usage strongly deviatesfrom SMS usage. For both types of traffic, color is used to
distinguish inbound vs. outbound traffic. The resulting shapeof the plot resembles lips and hence we call this type ofvisualization lip plots.
The patterns of these two plots are strikingly different.
Figure 4(a) shows data from a Saturday in one part of town.
The SMS traffic dominates the voice traffic, and the volumes
keep rising throughout the day with maximal usage between
11 PM and 1 AM. The voice traffic, despite being dominatedby the SMS traffic, has a noticable spike at 2AM. This is thecell tower antenna that points to the downtown area including
the majority of the restaurants and bars in town. The spikes
might represent late night revelers, and in particular the 2AM
voice spike might reflect the fact that the bars close at 2AM,
and patrons are looking for a ride home.
The plot in Figure 4(b) has quite a different pattern. This

plot is from a weekday and also shows a majority of datafrom SMS traffic as opposed to voice. But here the majorityof traffic is in the morning and in particular, we see spikes
in SMS usage at 7AM, 11AM and 2PM. Only at 2PM do
we see a similar spike in the voice traffic. This cell antenna
points towards the town’s high school, and could reflect thecommunication patterns of the students there, texting before
and after school and during lunch. The larger 2PM spike atthe end of the school day might reflect calls between students
and parents, where voice channels would be more likely.
In general (see http://bit.ly/BigLipPlot), we
found that there is a large heterogeneity in the patterns ofantennas pointing to different directions, reflecting differing
usage patterns in different parts of the city. We also founda wide variance in the volume covered by the different
directions. Certain directions simply have more traffic thanothers. In addition, small volume antennas have high variance,and often result in giving the lips a ‘fuzzier’ look. Finally,
the relationship between SMS and voice changes by direction,
especially on the weekends.
R EFERENCES[1] R. A. Becker, R. Caceres, K. Hanson, J. M. Loh, S. Urbanek, A. Varshavsky, and C. Volinsky. A Tale of One City: Using Cellular Network
Data for Urban Planning. IEEE Pervasive Computing, special issue on
Large-Scale Opportunistic Sensing, 10(4), October-December 2011.
[2] Census Transportation Planning Package (CTPP) 2000: Part 3. Downloaded from http://www.transtats.bts.gov.

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Segmentation of towns using call detail records
Romain Guigourès∗ and Marc Boullé∗∗ Orange Labs, 2 av. Pierre Marzin, 22300 Lannion, France
romain.guigoures@orange-ftgroup.com, marc.boulle@orange-ftgroup.com

Abstract—In this paper, we deal with the segmentation of
towns using call detail records. The data can be viewed as a directed bipartite graph wherein the source nodes correspond to
the towns, origins of the calls, and the target nodes that are thetowns, destinations of the calls. A nonparametric method basedon a Bayesian Approach is proposed to determine the finest
segmentation of these two sets. Instead of directly clustering
the nodes, we propose here to make a coclustering on the edges
defined as bidimensionnal items described by two features : thesource and target nodes. Once the finest clustering is obtained,
the clusters are successively merged on the two sets until only
one cluster remains, in such a way that the loss of information
is minimal. The initial segmentation is optimally coarsenedin order to enable a hierarchical exploratory analysis of thedata. Thus, it is possible to get insights either nationalwide or
locally. A study of the telephone areas of Belgium by exploring
the coclustering structure at different grain levels demonstrates
the interest of the method.
Keywords-Community detection; Clustering; Bayesianism;
Model Selection; Density estimation

I. I NTRODUCTION
Graph partitioning has long been studied in the operational research field. One of the oldest approaches is theminimum-cut method, where the graph is divided into apredetermined number of disjoint subsets, usually of approximately the same size, chosen such that the number of edges
between the clusters of nodes is minimized.
With the recent availability of many network data, suchas world wide web, social networks, phone call networks,science collaboration graphs [1], there is a renewed interestfor the graph partitioning problem, especially for the automatic discovery of community structures in large networks.
Many approaches have been studied for the problem of graphclustering, including hierarchical clustering, divisive clustering, spectral methods, random walk [2]. To evaluate thequality of a clustering regardless of the cluster number, themodularity criterion proposed in [3] is now widely acceptedin the literature, and has even been treated as an objectivefunction in clustering algorithms [4]. The modularity is ameasure ranging from -1 to 1, being all the more high thatthe clusters have more internal edges than the expected edges
number if the connections were made randomly, with thesame nodes degrees.
In this paper, we present a way of analyzing and summarizing the structure of large graphs, based on piecewise
constant edge density estimation. The approach extends thestochastic block modeling approach [5] in that the modelingmethod is fully non-parametric with the number of clusters
as a free parameter, and exploits a statistical model selection
technique and scalable optimization algorithms. Data gridmodels [6] are applied to graph data, where each edge isconsidered as a statistical unit with two variables, the source
and target nodes. The objective is to find a correlation model
between the two variables, owing to a data grid model, whichin this case turns to be a coclustering of both the source andtarget nodes of the graph. The cells resulting from the crossproduct of the two clusterings summarize the edge density
in the graph. The best correlation model is selected using the
MODL (Minimum Optimized Description Length) approach[6], and optimized by the means of combinatorial heuristics
with super-linear time complexity.
Then, a post processing technic is introduced consisting
in merging successively the clusters in the least costly way,from the finest clustering model down to one single cluster
containing all the towns. It appears that the cost of themerge of two clusters is a weighted sum of Kullback-Leibler
divergences from the merged clusters to the created cluster
which can be interpreted as a dissimilarity measure between
the two clusters that have been merged. Thus, the postprocessing technique can be considered as an agglomerative
hierarchical clustering [7].
The rest of the paper is organized as follows. In Section 2,
we present the MODL approach for data grid models applied
to the edge density estimation in graphs and the postprocessing enabling the exploratory analysis. Then in Section
3, experiments on Belgian call detail records illustrate theproperty of the method. Finally, Section 4 concludes thepaper and introduces the future works.
II. T HE SEGMENTATION
A. Graph clustering using MODL
Unlike making clustering on a simple graph like themodularity-based method do, the graph we deal with isdirected, bipartite and with multiple edges. The source nodes
are the towns, origins of the call, the target nodes the towns,
destinations of the calls and the edges the calls. Figure 1
illustrates the different data representations.
The towns are grouped if the distributions of the calls
are similar. This means that instead of making groups oftowns that frequently call each other, the method brings
together the towns that call the same towns and in the same

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(a)

(b)

given the data D, obtained by maximizing a criterion built onprior terms P (M ) which give priority to simple models, i.e.
with a low number of clusters, and on the likelihood whichfavours the informative models, i.e. fine grained models :P (M )P (D|M )M ∗ = argmax P (M |D) = argmax
P (D)
M
M
⇒ M ∗ = argmin(− log P (M ) − log P (D|M ))M(c)Figure 1: Representation of the tabular data displayed inFig.(c) as a simple weighted graph (Fig.(a)) and as a directedbipartite multigraph (Fig.(b))

ratio. The objective of the method is to estimate the densityof the edges owing to a coclustering of the sources andtarget nodes. Figure 2 illustrates such a coclustering with
two source clusters and two target clusters. In this example,
the probability of edges from Brussels or Liège to Brusselsor Namur is 50%.

The detailed criterion is left out for brevity. The full criterion
is described in [8]. As for the optimization algorithm,
we have used the optimization heuristics detailed in [9],which have practical √scaling properties, with O(m) spacecomplexity and O(m m log m) time complexity, with m
the number of edges. The main heuristic is a greedy bottomup heuristic, which starts with a fine grained model, considers all the merges between clusters and performs thebest merge if the criterion decreases after the merge. The
process is reiterated until no further merge decreases thecriterion. This heuristic is enhanced with post-optimization
steps (moves of towns across clusters), and embedded into
the variable neighborhood search (VNS) meta-heuristic [10],which mainly benefits from multiple runs of the algorithmwith different random initial solutions. The optimization algorithms summarized above have been extensively evaluatedin [9], using a large variety of artificial datasets, where thetrue data distribution is known.
B. Merging the clusters

Figure 2: Example of coclustering
Formally, a model M of edge density estimation is definedby :• the number of source and target clusters• the partition of source (resp. target) nodes into source(resp. target) clusters.
• the edges distribution on the coclusters defined as the
cross-products of the source and target clusters.
• for each source (resp. target) cluster, the edges
distribution on the node of the cluster.
The coarsest model is based on one single cluster oftowns, whereas the finest one exploits one cluster per town.
Coarse grained models tend to be reliable, whereas finegrained are more informative. The issue is to find a trade-off
between the informativeness of the edge density estimationand its reliability, on the basis of the granularity of thecoclustering. Applying a Bayesian model selection approach,the best model M ∗ is defined as being the most probable

In case of large datasets, i.e. with a huge number of edges,
the edge density converges to the true edge distribution.
This means that, for each town, the distribution of thecalls is fine enough to be differentiated. Thus the method
yields one town per cluster, that is too fine for an easyinterpretation. To overcome this issue, a post-processing
technique is proposed. It consists in merging successivly theclusters so as to worsen the least the criterion. By studying
in detail the variation of the criterion due to the merge,
it appears that the merge of two clusters is all the more
likely that the distributions of their in/outcoming calls are
similar. Figure 3 illustrates two towns very likely to bemerged because of their similar distributions. Technically,
this variation is a sum of Kullback-Leibler divergences fromthe merged clusters to the resulting one, weighted by thesize of each of them. In brief, this process is equivalent
to making a hierarchical agglomerative classification, whose
dissimilarity measure is based on probability divergences.
III. E XPERIMENTS
Experiments have been conducted on call detail recordsof the Belgian telecommunications company Mobistar aggregated on 6 months. There are 217 millions calls between
589 towns. Another approach has been applied on the samedataset in [11], which results in 17 clusters. Like in this study

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(a)

(b)

Figure 3: Similar distributions of the calls of two germanspeaking Belgian towns.

our clusters are geographically connected. However ourclustering results enable a multiscale exploratory analysisof the Belgian telephone areas.
A. The Finest Clustering
The finest clustering highlights 588 groups over Belgium.
Hence, each cluster is made up of one town, except one
cluster that groups two towns together. Given the huge
number of calls (217 millions for 589 cities), the finest
clustering on this dataset has reliably approximated the true
distribution. This is shown on Figure 4, the clustering is all
the more fine that there are edges.

Figure 4: Number of clusters retrieved by the method for agiven subset of randomly selected edges

B. Two linguistic communities
In this experiment, the clusters have been merged successivly until obtaining two clusters. This segmentationin two groups highlights the two linguistic communities
of Belgium: Flanders and Wallonia. This reveals that the
distribution of the calls of a town is denser in the areas withthe same linguistic characteristics. The case of Brussels isparticular, because the majority of the inhabitants of the city
are french-speakers despite it is included into the Flemish
territories. That is why the region of Brussels has beenclustered into Wallonia.

Figure 5: Segmentation of Belgium into two clusters

C. Eleven clusters that do not match with the provinces
boundaries
There are eleven provinces in Belgium, five in Flanders
and five in Wallonia, the eleventh being the province ofBrussels-Capital. In order to compare the delimitation of
the telephone areas and the boundaries of the provinces, wehave studied the clustering with eleven clusters. The clusters
are displayed on a map in Figure 6.
For Antwerp, East and West Flanders, the clusters fit well
the provinces territories.
The provinces of Hainaut and Liège are splitted into
three clusters. For the first one, it can be explained by thepresence of some major cities in the same province (Mons,
La Louvière and Charleroi). For the second one, we cannotice the sphere of influence of Liège while the area east of
the city corresponds to the arrondissement of Verviers wheremore than 25% of the inhabitants are German-speakers.
There are also clusters that straddles some provinces, likethe cluster grouping the arrondissement of Leuven and theprovince of Limburg or the one grouping the province of
Luxembourg and a part of the provinces of Namur and Liège.
These telephone areas are consequently vast, that is why afiner and local study would yield enough clusters to make arelevant exploratory analysis.
The case of Brussels highlights the correlation between
the calls and the sphere of influence of the city including
a little part of the Flemish Brabant and almost all the
Walloon Brabant. This can be explained by the current
trend of expansion of the suburbs to a southern direction,
towards Walloon-Brabant, the inhabitants of Brussels being
attracted by more peaceful areas with the same linguistic
characteristics [12].
D. A local study of Brussels Region
Brussels is a particular city. The capital of Belgium isvery cosmopolitan. In spite of the predominance of thefrench-speaking community, it is included into the Flemish
territories. A study of the calls from the towns of theprovince of Brussels-Capital to all the Belgian towns allows
the segmentation of Brussels and its suburb according to the

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Figure 6: Segmentation of Belgium into eleven clusters

calls the users made all over Belgium. Merging until threeclusters (over 19 towns) highlights interesting groups. The
first group that is colored in pink in Figure 7 is located on
the West side of the downtown and globally corresponds
to the disadvantaged neighborhoods of Brussels while thegreen group highlights the privileged south-east quadrant ofBrussels [12]. As for the two towns colored in Orange, Uccle
and Ixelles, the higher education institutions are relativly
concentrated there.

is proposed. This technique aims at merging successivly
the clusters until obtaining a simplified clustering whileworsening the least the model.
Experimentations on a Belgian dataset show the varietyof the possible analysis. The finest study yields almost
one town per cluster. In order to make a global analysis,
the model is coarsened by merging the clusters. With two
clusters, the linguistic communities are well segmented
while the province boundaries do not match with the clusters
delimitations when the clusters are merged until elevengroups. Local groupings based on the call made all over
Belgium are also possible and illustrated by the example ofBrussels and its suburbs.
Because the method is based on a density estimation, thefuture works will be extended to the dynamic graphs byadding a third temporal variable. This would enable a studyof the temporal evolution of social networks and yield the
optimal discretization into time slots.
R EFERENCES[1] R. Albert and A.-L. Barabasi, “Statistical mechanics of complex networks,” Reviews of Modern Physics, vol. 74, p. 47,
2002.
[2] S. E. Schaeffer, “Graph clustering,” Computer Science Review, vol. 1, no. 1, pp. 27 – 64, 2007.
[3] M. Girvan and M. E. J. Newman, “Community structure insocial and biological networks,” Proceedings of the National
Academy of Sciences, vol. 99, no. 12, pp. 7821–7826, 2002.
[4] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” 2008.
[5] S. Wasserman, G. Robins, and D. Steinley, Statistical Network
Analysis: Models, Issues, and New Directions, 2007.
[6] M. Boullé, “A bayes optimal approach for partitioning thevalues of categorical attributes.” Journal of Machine Learning
Research, vol. 6, pp. 1431–1452, 2005.
[7] T. Hastie, R. Tibshirani, and J. Friedman, The Elements ofStatistical Learning. Springer, New York, 2002.

Figure 7: Segmentation of Brussels-Capital into three clusters

IV. C ONCLUSION
In this paper, we have focused on graph clustering applied
to a telephone dataset. The method allows the discovery
of structures in graphs. By clustering the source and target
nodes while selecting the best model according to a Bayesian
approach, the method behaves as a nonparametric estimatorof the edge density. In case of large graphs, the bestmodel tends to be too fine grained for an easy interpretation. To overcome this issue, a post-processing technique[8] M. Boullé, “Nonparametric edge density estimation in large
graphs,” Orange Labs, Tech. Rep., 2011.
[9] ——, Data grid models for preparation and modeling insupervised learning. Microtome, 2010.
[10] P. Hansen and N. Mladenovic, “Variable neighborhood
search: principles and applications,” European Journal ofOperational Research, vol. 130, pp. 449–467, 2001.
[11] V. D. Blondel, G. Krings, and I. Thomas, “Regions andborders of mobile telephony in belgium and in the brussels
metropolitan zone,” the e-journal for academic research on
Brussels, 2010.
[12] C. Kesteloot, C. Vandermotten, and B. Ippersiel, “Dynamic
analysis of troubled neighbourhoods in the belgian urban
regions,” 2007.

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Understanding the evolution of spatial structurein urban communities
Fergal Walsh and Alexei Pozdnoukhov
National Centre for Geocomputation
National University of Ireland Maynooth{fergal.walsh,alexei.pozdnoukhov}@nuim.ie
http://ncg.nuim.ie

This work explores the evolution and spatial organization of urban communities in a daily cycle of a city using scalable social network analysis methods.
Using mobile phone records we investigate calling and movement patterns ofabout 500.000 citizens of Dublin in the course of a typical weekday. The analysis
reveals spatial patterns in community structures at fine spatial scales which aremost likely related to the activities typical to the different functional regionsof a city. To our knowledge this is the most detailed analysis, in terms of both
spatial and temporal resolution, carried out to date on this type of data.
We combine two methods adopted from the study of complex networks to
examine the time varying spatial structure of communities in an urban environment [1], [2]. The spatial structure of these communities is observed from theanalysis of the short-term aggregates of traffic on a mobile telephone network.
The analysis is based on an origin destination matrix of calls and text messages
between customers.
In our case study we use the cells of the network as the spatial areas rather
than administrative boundaries. Cells in urban areas are typically very small
due to high population densities and so offer high spatial resolution. Rather
than using the customer’s home address, each end of the call is geo-located tothe cell used at the time of the call. The mobile phone network is by definition
dynamic, with phone users constantly moving. We use time-of-call locationsin an attempt to capture these dynamics. Finally, and most importantly, we
perform community detection at multiple snapshots in time, and then analyse thechange in community composition [2], [3] . We use a combination of geographical,
temporal and graphical layouts linked together in a single view to visualise andinterpret these changes (Figure 1).
We observe that different communities are found at different times of day,especially in urban areas where the population and communication habits varywith the time of day. A distinct pattern of transitions and transformations of thecommunities at the temporal scales of several hours and sub-kilometer distances
is observed [4].

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(a) 00:00 - 01:59(b) 02:00 - 07:59(c) 08:00 - 09:59(d) 10:00 - 11:59(e) 20:00 - 21:59(f) 22:00 - 23:59(g) Alluvial timeline diagram of community membership. Each line represents a single
cell. Each column represents a time period, as labeled.

Fig. 1: The daily cycle of community structure in the Dublin region.

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It is important to understand the forces behind these changes. Are we seeing
these changes because people are moving in space or is it because people communicate with different people and different places over the course of the day?
Of course the answer is a bit of both, but we attempt to quantify this by doing a
combined analysis of communications and physical movements. We show exactly
how much physical movement there is between each time step. We also quantify
the influence of time of day on the calling patterns of each user. From these two
measures we can try to understand the causes of change in spatial structure ofcommunities.
Our combined fine resolution movement-communication analysis has several
implications for understanding urban structure. It is able to capture the fundamental properties of human behavior in terms of communication and transportation habits, as well as opening ways to aid decision making in introducing social
policies, optimise resource allocation and refine planning of the city infrastructures. Particularly, we observe a distinct North-South divide that splits the city
into two parts, separated both in terms of communication and physical movement. We observe the existence of small distinctive communities showing poor
communication with the outside world that might indicate ongoing alienationand segregation processes. Generally, these findings also show a further need toextend the typology of community evolution events used in state-of-the-art network evolution models to implicitly incorporate spatial relations at a variety of
temporal scales.
Acknowledgments. Research presented in this paper was funded in part throughStokes Lectureship programme and Strategic Research Cluster grant (07/SRC/
I1168) by Science Foundation Ireland, and faculty research awards from Google
and IBM. The authors gratefully acknowledge this support. We would also liketo gratefully acknowledge Dr. R. Farrell and the support of Meteor for providing the data used in this paper, in particular Helene Graham, John Bathe andAdrian Whitwham.

References
1. Blondel, V., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment
2008, P10008 (2008)
2. Greene, D., Doyle, D., Cunningham, P.: Tracking the evolution of communities indynamic social networks. In: Proc. International Conference on Advances in Social
Networks Analysis and Mining (ASONAM’10) (2010)
3. Rosvall, M., Bergstrom, C.: Mapping change in large networks. PLoS One 5(1),e8694 (2010)
4. Walsh, F., Pozdnoukhov, A.: Spatial structure and dynamics of urban communities(June 2011), the First Workshop on Pervasive Urban Applications (PURBA)

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

59Trace-driven analysis of data forwarding inopportunistic networks
Merkourios Karaliopoulos

Panagiotis Pantazopoulos

Eva Jaho

Ioannis Stavrakakis

Department of Informatics and Telecommunications
National & Kapodistrian University of Athens
Ilissia, 157 84 Athens, Greece
Email: {mkaralio, ppantaz, ejaho, ioannis}@di.uoa.gr

Abstract—We summarize undertaken and ongoing work on the
direct and exclusive use of mobile phone traces for assessing theperformance of different opportunistic forwarding schemes. Our
methods draw on graph-expansion techniques and circumventthe need for more custom simulation software packages. They
address a wide range of opportunistic dissemination schemes
including controlled flooding and socioaware protocol variants.
We outline the general approach and exemplify it with anassessment of centrality metrics as drivers of data dissemination
decisions. Finally, we report results on the benefits of identifying
community structure out of the similarity of interests across theopportunistic network and discuss their implications for tracebased evaluation.

I. I NTRODUCTION
Both the motivation and concerns for the use of real data
traces in evaluating protocols and algorithms relate exactly to
the words ’real’ and ’trace’. On the one hand, they promise
realistic performance evaluation and credible results whencompared to synthetic input data. On the other hand, they
always raise concerns about their representativeness and thegenerality of the evaluation results. Nevertheless, the use ofsuch traces has become the de facto approach to the evaluationof data dissemination in user-oriented network paradigms suchas the opportunistic networking.
We report herein work we have been carrying out on tracebased performance evaluation of different opportunistic forwarding schemes. Our methods draw on graph-expansion techniques and circumvent the need for more custom simulation
software packages. They address a wide range of opportunistic
dissemination schemes coming under the controlled flooding
family of protocols and can be extended to socioaware protocol
variants. We focus, in particular, on two main directions
that socioaware protocol design in opportunistic networks hastaken: the introduction of social metrics into the individual
node-oriented relaying utility functions (e.g., [1]), and the explicit a priori assumption that such networks avail community
structure that can be detected and exploited in disseminating
data (e.g., [2]).
II. C OMPUTATION OF SHORTEST PATHS OVER TRACESThe computation of shortest path(s) for a given message
proceeds in three sequential processing steps that differentiate
depending on the forwarding scheme and whether shortest
paths correspond to minimum-delay or minimum-hopcount
The work referred to in this abstract has been supported in part by theEuropean Commission IST-FET project RECOGNITION (FP7-IST- 257756)
and the Marie Curie grant RETUNE (FP7-PEOPLE- 2009-IEF-255409).

space-time paths. In every case, input to this process are timeordered traces of node contacts, i.e., sequences of contact
records with the general format shown in Fig. 1(a). Eachcontact record c = (n1 , n2 , ts , te ) includes four fields: thetwo nodes that meet, the time their encounter starts, ts , andthe time the encounter ends, te ; the difference of the last two
fields corresponds to the contact duration. The fourth field
becomes redundant under the infinite link capacity assumption,i.e., messages need minimal (zero) time to traverse a link
between two nodes once this becomes available to them.
1) From the original contact trace to the forwarding contacts: In this step, the original full contact trace is filtered with
criteria that account for the different opportunistic schemes
so that only those contacts that can result in forwarding of
data, hereafter called forwarding contacts, are retained. If ts
is the time a message becomes available at source node sfor destination node d, then the filtering step first excludes
all contact records up to the first one c0 = (s, n, ts ) involving
node s after time ts . It then initializes an ordered list, hereafter
called forwarding list, with the nodes s and n. The forwarding
list stores at each time potential forwarders of the message,
nodes that have acquired the message and may, depending on
who they encounter, forward it further.
Contact records after c0 are scanned sequentially. These
contacts may belong to one of three typologies, depending on
the encountered nodes: (a) neither node lies in the forwarding
list; (b) both nodes are already listed in the forwarding list;
and (c) one of the two nodes is in the forwarding list (1entry contacts). Contacts of the first type do not contribute to
the forwarding process and are ignored. On the other hand,contacts of the second type do not represent real additional
forwarding opportunities since the assumption in all schemesis that nodes with a message copy will forward it to a nodethat does not have it and is eligible to acquire it upon thefirst encounter with it. The most interesting type of contacts
is the third one, whose manipulation directly depends on theopportunistic forwarding scheme.
For example, let us consider the manipulation of 1-entry
contacts for the two-hop scheme, where nodes other than themessage source availing a message copy cannot forward it
but only to the destination node. Therefore, two types of 1entry contacts are retained: those (s, ∗, t), t ≥ ts involving thesource node s as the single node already logged down in theforwarding list, as well as the first 1-entry contact involving
the destination node. All other 1-entry contacts are filtered outof the trace (contacts C1 -C6 in Fig. 1(b)). In fact, when we

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

60are after minimum-delay space-time paths, the filtering step
terminates upon the first appearance of the destination node din an 1-entry contact.
2) Building the forwarding contact graph: Outcome of thefirst processing step is the reduced set of contact records
corresponding to forwarding contacts. The next step is toderive the graph construct Gc = (Vc , Ec ) that can capture
these contacts and their timing relationship. The constructdraws on the "temporal” graph representation in [3].
For forwarding schemes under the controlled flooding category, the graph construct is built out of the first contact c0
and 1-entry forwarding contacts occurring thereafter. Each oneof them adds to the graph: a) a pair of vertices, one for eachnode involved in the contact; b) one space-spanning directededge connecting the two encountered nodes; and c) one timespanning directed edge towards the node that is alreadyincluded in the forwarding list, originating from the vertex that
represents its most recent forwarding contact. Hence, every
time a node v ∈ V appears in an 1-entry forwarding contact,
it generates a new vertex vc ∈ Vc for construct Gc . As a result,each network node v ∈ V is eventually identified with a single
global index in [1, |V |] for the node set V and the forwarding
list, and multiple non-successive indices for the construct Vc .
The graph edge set Ec is weighted. When we are interested in minimum-delay space-time paths, time-spanningedge weights equal the time differences between successive
occurrences of the node in forwarding contacts and express
the time over which a message may be stored and carried bya given node. Space-spanning edges express the time it takes
to forward a message upon a contact and, under the infinite
link capacity assumption, are assigned zero weights. On thecontrary, when we are interested in minimum-hopcount spacetime paths, time-spanning edges are assigned zero weights andspace-spanning ones unit weights. The Gc constructs resulting
from the contact trace 1(a) for the two-hop forwarding scheme
is given in Fig. 1(c).
3) Computing shortest paths: The last processing step
consists in the computation of shortest space-time paths overthe expanded graph Gc . The size of the graph depends onwhether we want to compute minimum-delay or minimumhopcount space-time paths. When we are after the minimumdelay path(s), the parsing of contacts ends upon the first
appearance of the message destination node d in an 1-entry

contact. This may be anywhere from the first till the (|V |−1)thcontact that is retained in the set of forwarding contacts. On
the contrary, to include all possible minimum-hopcount paths,
the parsing should continue until either all network nodes enter
the forwarding list or the source nodes contact the destination
node directly, whatever happens first. It can be shown thatthe graph constructs Gc are directed acyclic graphs (DAGs)
and running Dijkstra will yield the s-d minimum-hopcountspace-time path in O (|Vc |+|Ec |)log2 |Vc | = O(|V |2 log2 |V |)time [4].
III. C ENTRALITY- BASED DATA DISSEMINATION
Our trace-based performance evaluation approach can beapplied to socioaware opportunistic forwarding schemes. SimBetTS [1] and BubbleRap [2] protocols compute metrics
borrowed from Social Network Analysis (SNA) over contact
graphs, which effectively aggregate the sequence of nodeencounters over certain time windows T . Both protocols haveidentified betweenness centrality (BC) as the dominant usercentric metric, even when it is combined with more metricwhen making forwarding decisions.
Centrality computation caveats: There are three mainconcerns regarding the realization of a user-centric approachrelying solely on BC. First of all, node centrality values
are destination-agnostic; namely, the node relaying utilities
are averages computed across all node pairs in the network. Secondly, SNA metrics are computed over graphs. The
derivation of these graphs out of the sequence (history) of
contacts has been shown to be highly sensitive to the time
window T during which all past contacts are aggregated into
a contact graph [5]. Thirdly, the original centrality metrics
need to be approximated by egocentrically computed centrality
variants [6], which, in principle, offer only limited views of thenode’s utility in the network. We have employed real humanmobility traces of pairwise node encounters to experimentally
study these three factors and their impact on the BC-based
data dissemination [7].
Mobile traces: We have used five well-known experimental
traces, part of the iMote-based traceset available in [8]. The
traces cover a rich diversity of environments with an experimental period from few days to almost one month. All traces,
gathered over the last five years, include Bluetooth sightingsof users carrying iMotes. Each Bluetooth sighting is assumed
t0,scontact
id...
C0

involved nodes...
n2

contact
start time
...
t0,s

contact
end time...
t0,eadditional
fields…
...

contact
id
...
C0

...
n1

C1

...
n2

contact
start time
...
t0,s

contact
end time...
t0,eadditional
fields
…
…

...
n1

n3

n4

t1,s

t1,e

...

C1

n3

n4

t1,s

t1,e

…

C2

n4

n5

t2.s

t2,e

...

C2

n4

n5

t2.s

t2,e

…

C3

n2

n5

t3,s

t3,e

C4

n5

n3

t4.s

t4,e

...

C3

n2

n5

t3,s

t3,e

…

...

C4

n5

n3

t4.s

t4,e

…

C5

n3

n2

t5,s

C6

n3

n6

t6.s

t5,e

...

C5

n3

n2

t5,s

t5,e

…

t6,e

...

C6

n3

n6

t6.s

t6,e

C7

n1

n4

…

t7,s

t7,e

...

C7

n1

n4

t7,s

t7,e

...

C8
...

n2
...

n6
...

t8,s
...

t8,e
...

...
...

C8
...

n2
...

n6
...

t8,s
...

t8,e
...

...
...

(a) Original trace.

involved nodes(b) Forwarding contacts: two-hop forwarding.

t8,s

t7,s
0

n1
0 1
n2

t7,-t0,s0t8,s-t0,s

0

1

n3
0

n4
n5
n6

(c) Two-hop forwarding graph.

Fig. 1. Original contact trace, forwarding contacts (entries in bold), and resulting graph construct for message m = (1, 6, t0,s ): two-hop forwarding scheme.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

61

1

TABLE I
C HARACTERISTICS OF EMPLOYED DATASETS
Configuration
Device type
Network type
Duration (days)
Scan time (sec)
Granularity (sec)
Mobile Devices
Stationary Dev.
External Dev.
Average internal
contacts/pair/day# of Contacts

Intel
iMote
B/T
6
5-10
120
8
1
119

Cambridge
iMote
B/T
6
5-10
120
12
0
211

Infocom05
iMote
B/T
4
5-10
120
41
0
233

Content
iMote
B/T
24
5-10
120-600
36
18
11368

Infocom06
iMote
B/T
4
5-10
120
78
20
4421

9.09
2766

12.09
6732

8.60
28216

0.66
41330

9.03
227657to be a contact where nodes can exchange information. In
Table I scan time is the time needed by iMotes to perform acomplete scan for Bluetooth devices and takes approximately
5 to 10 seconds; time granularity represents the idle time
between two consecutive scans and affects significantly themeasurement accuracy. We analyze only the contacts between
iMotes (i.e., internal contacts), which represent the data transfer opportunities among participants.
Emulation of optimal routing over the traces: The theoretically optimal paths (of minimum delay and hopcount)to the destination, have been computed directly out of thedataset sequence of encounters according to the expandedgraph technique, introduced in Section II. The outcome values
are naturally considered as performance benchmarks.
Emulation of BC-based routing over the traces: To accountfor the relative social standing of each node we need toaggregate the encounters’ history to an unweighted or a more
“informed” weighted static graph with link weights equal to
the frequency of contacts, over which the centrality values
are computed. The trace is again replayed (sequentially read)but now we compute five different centrality-variants for eachcontact record; a message is forwarded provided that theencountered node exhibits higher value of the corresponding
variant than the one of the current holder. These values may
be either the sociocentric (including the destination-aware BC
variant called Conditional Betweenness Centrality (CBC) [9])or their egocentric counterparts [6] computed over the full setof contacts within the past T time window.
Summary of results: We have generated messages with randomly chosen source and destination and emulated their paths
over the traces. The message delivery delay and number offorwarding hops have been computed and compared with thoseof the optimal (opt) scheme described above. Our findings
are summarized in the following discussion. The centralitybased approaches perform considerably worse than the optimal
method both in terms of message delay and hops to thedestination. Forwaring performance across the different traces
depends on the way nodes’ mobility patterns mix with eachother. Less intuitively, replacing BC with its destination-aware
counterpart (CBC) does not give benefits in terms of message
delay but results in significant energy savings by reducing
the message hops. When computing BC over weighted graphs
our study reports that the performance does not consistently
improve for all traces. Nevertheless, the routing protocol ismore resilient to variations of the time window used forcontact aggregation. Finally, we have found that using theegocentric BC variant penalizes the forwarding performance

only marginally, when computed over unweighted and evenless when computed over weighted graphs. This is further
supported by the observed strong positive correlation between
socio- and egocentric BC in almost all traces. A more detailed
discussion as well as explanatory plots appear in [7].
IV. E NRICHING TRACES WITH USERS ’ INTERESTS
In [10], we have enhanced primitive push mechanisms
for data dissemination in opportunistic settings with social
information concerning the preferences and interests of network nodes. It is shown that interest-based forwarding can
improve considerably the information dissemination process.
Inspired by this result, in [11] we have proposed a framework
called ISCoDe, which identifies communities of nodes withsimilar interests. We have applied ISCoDe to the Delicious(www.delicious.com) platform, showing how end-user interests can be inferred out of a real online social networking(OSN) application.
Adding further realism to this thread would call for data
traces that, besides encounters, provide information about thepreferences of users. Enriching mobile phone datasets withsuch information is one option that could be considered infuture datasets to become available. This information encodedin the form of user preference distributions (profiles) over a
set of certain thematic areas (such as music, sports, art), canbe inferred out of tags annotating data that users save in theirmobile phones. A more demanding alternative would be toindirectly infer such information from online social networks,
trying to correlate traces of encounters with OSN user profiles(e.g., [12]).
R EFERENCES[1] E. M. Daly and M. Haahr, “Social network analysis for information
flow in disconnected delay-tolerant manets,” IEEE Trans. Mob. Comput.,vol. 8, no. 5, pp. 606–621, 2009.
[2] P. Hui et al., “Bubble rap: Social-based forwarding in delay tolerant
networks,” IEEE Trans. Mob. Comput., (To Appear) 2011.
[3] V. Kostakos, “Temporal graphs,” Physica A-statistical Mechanics and
Its Applications, vol. 388, pp. 1007–1023, 2009.
[4] M. Karaliopoulos, “Trace-based performance analysis of opportunistic forwarding under imperfect cooperation conditions,” University ofAthens, Tech. Rep., July 2011.
[5] T. Hossmann, T. Spyropoulos, and F. Legendre, “Know thy neighbor:
Towards optimal mapping of contacts to social graphs for dtn routing,”
in Proc. IEEE Infocom 2010, San Diego, CA, USA, Mar 2010.
[6] P. Marsden, “Egocentric and sociocentric measures of network centrality,” Social Networks, vol. 24, no. 4, pp. 407–422, October 2002.
[7] P. Nikolopoulos, T. Papadimitriou, P. Pantazopoulos, M. Karaliopoulos,
and I. Stavrakakis, “How much off-center are centrality metrics forrouting in opportunistic networks,” in ACM MobiCom 2011 CHANTS
Workshop), Las Vegas, NV, USA, Sep 2011.
[8] J. Scott, R. Gass, J. Crowcroft, P. Hui, C. Diot, and A. Chaintreau,“CRAWDAD data set cambridge/haggle (v. 2009-05-29),” Downloaded
from http://crawdad.cs.dartmouth.edu/cambridge/haggle, May 2009.
[9] P. Pantazopoulos, I. Stavrakakis, A. Passarella, and M. Conti, “Efficient social-aware content placement for opportunistic networks,” inIFIP/IEEE WONS, Kranjska Gora, Slovenia, February, 3-5 2010.
[10] S. Allen, M. Chorley, G. Colombo, E. Jaho, M. Karaliopoulos,
I. Stavrakakis, and R. Whitaker, “Exploiting user interest similarity andsocial links for microblog forwarding in mobile opportunistic networks,”
in Elsevier Pervasive and Mobile Comuting (submitted), 2011.
[11] E. Jaho, M. Karaliopoulos, and I. Stavrakakis, “ISCoDe: A framework
for interest similarity-based community detection in social networks,”
in Proc. 3rd Int’l Workshop on Network Science for Communication
Networks, 2011.
[12] G. Bigwood and T. Henderson, “Bootstrapping opportunistic networks
using social roles,” in Proc. 5th IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications (AOC), 2011.

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Statistical detection of information flow traces in alarge phone call dataset
Lionel Tabourier∗
LIP6, Université Pierre et Marie Curie, Paris.
†

Fernando Peruani

Max Planck Institute for the Physics of Complex Systems, Dresden.

Abstract
The content of phone calls is in general out of reach for confidentiality reasons. In addition, the massive amount of information exchange in large mobile
phone networks makes in practice impossible to analyze every conversation inorder to identify how information flows. That is why it would be of great importance to design ‘content-free’ methods to identify where and when information
propagation takes place.
Here we propose a method to address this issue that combines dynamicalmeasurement tools with appropriate comparison models. Our method relies on
the assumption that intentional information propagation implies strong causality effects. In our analysis, phone calls are intended to transmit information andinvolve a causality directionality: from the caller to the callee. The transmissionof information in the opposite direction, while clearly possible, does not imply
an intentionality and consequently causality. This simple assumption leads us
to take into account the directed character of the mobile phone data. By implementing the same measures on a real dataset and null models, we are able
to identify when and where causality effects impact the information spreading
statistics.
We used for this study a large cellphone record providing who calls whom andwhen. It consists of calls among 1 million subscribers of the same mobile phonecompany over a period of a month. As we are interested in the broadcasting of
information, we focus on actual calls where the caller indeed reach the callee,
restricting the dataset to around 14 million events.
We run on the database a set of statistical tools and collect statistics on
several dynamical patterns. A central measurement of this work is the amountof cascade-like patterns: if a node is ‘activated’ during a period τ after being
called and may activate other nodes by calling them, we describe the obtained∗†

lionel.tabourier@lip6.fr
peruani@pks.mpg.de

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63patterns as cascades — or causality trees — which may be seen as proxies of
information propagation processes.
We first lay stress on the importance of taking into account the directedness ofsuch data: by combining statistics on causality trees with theoretical calculations,
we prove that the spreading processes are highly dependent on the in-out degreecorrelations exhibited by the users. We also learn that a given information, e.g.
a rumor, would require users to retransmit it for more than 30 hours in order tocover a macroscopic fraction of the system.
Then, we build models that are obtained by randomizing a part of the original
dataset so that we keep the ingredients which are essential for the understandingof the communication behavior over the whole network. Two comparison models
are proposed: the first one consists in randomizing the timestamps of events
over the whole dataset. It is a well-known fact that diffusion phenomena widely
depends on temporal patterns such as day-night periodicity or bursting activityof users. However, this trivial model yields results on statistics which roughly
reproduces the behavior in the real dataset, so that it can be used as a baseline
to compare measurements on the real network and on a more precise model.
The second comparison model aims specifically at detecting traces of diffusion
phenomena. In both its purposes and features, it has to our knowledge no equivalent in the literature: it is supposed to keep all the characteristics of the original
dataset except for the causality link that may exist between a phone call received
and a phone call given. The calling activity of each individual remains indeed
unchanged as well as the destinations of the calls but the correlation possibly
existing between the phone calls given by two different users are broken.
By focusing on subtle features of the dynamical data, this model reveals thatcausality effects are only visible as local phenomena and during short time-scales— in this context no more than a few hours — making information flow tracesin such dataset hard to detect. However, we show that node-node correlations of
the underlying social network, while allowing the existence of information loops,
promote information spreading at short range and during short time-scales. It
is indeed possible to discover very specific motifs, such as star-like and cycle-likecommunication patterns, which are underestimated by the model, and we canassess the probability that such patterns are related to a diffusion process.
The method that we propose is thus a step forward in locating where and when
information flow happens in a phone call dataset without resorting to the content
of the calls. As it only relies on having a large sequence of timestamped events,
we suggest that such analysis could be used in other contexts of information
spreading over large communication datasets, such as emails exchanges or instantmessaging networks.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

64Socially-mediated diffusion via cell phones: an analysis of ringback diffusion
in a cell phone network
Isar Kiani1, Derek Ruths2, Thomas Shultz3

1 John Molson School of Business, Concordia University
2 School of Computer Science, McGill University
3 Department of Psychology, McGill University

The ringback is a recent innovation introduced by some cell phone service providers
which permits a cell phone owner to select the ringtone another individual hears
when calling that person’s cell number. Like many features introduced in themobile service market, the ringback provides a way for customers to personalize thecell phone experience that they and members of their social circles have.
In some implementations of such a system, the primary way to acquire a ringback is
to copy it from another user who already has it. This social rule for ringback
diffusion combined with the singular utility of ringbacks as an acoustic social
gesture from the callee to the caller makes the spread of ringbacks through a cell
phone calling network a useful phenomenon for understanding the larger issue of
how social content propagates through mobile networks.

In this project, we used a data set from a leading telecommunication company in the
Middle East to characterize the diffusion profiles of 3,434 ringbacks. The dataset
was collected over a 2.5 month period, from August 19 to October 31, 2010. It
consists of 944,271 ringback adoption events among 1,261,044 distinct users. The
service provider specifies a classification of each ringback into one of four
categories: pop, film and TV, sports, and religion.

We find that, despite ringback content and introduction times being different, mostringback diffusion curves can be categorized into a small number of well-supported
curves. We use Kullback-Leibler divergence to obtain pairwise diffusion curve
distances and, after clustering, explore several ringtone features that might explain
diffusion patterns.

In addition, we also consider the individual ringback diffusion networks. Here, too,
we can cluster ringbacks according to those whose networks share similar
topological features including degree distribution, motif densities, and diameter.
We find that ringback network structure also can be classified into a relatively small
number of classes.

Our preliminary evidence suggests the presence of diffusion mechanisms that are
well-conserved across subsets of ringbacks and that attributes of individual
ringbacks influence the mechanisms that are responsible for their eventual diffusion
through the cell phone network.

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66

Estimating Dynamic Urban Population ThroughAssimilated Mobile Phone Data
Teerayut Horanont

Ryosuke Shibasaki

Institute of Industrial Science, University of Tokyo
E-mail: teerayut@iis.u-tokyo.ac.jp

Center for Spatial Information Science, University of Tokyo
E-mail: shiba@csis.u-tokyo.ac.jp

Abstract—Up to this day, the understanding of real-time
population distribution remains highly questionable andproblematic. Therefore, this research provides a novel method inorder to capture of large-scale quantitative data related tohuman mobility. We developed techniques using mobile phone
Call Detail Records (CDRs) and trip survey data to provide anestimated population at high temporal resolution. The trip
surveys are served as unbiased movement samples while mobile
phones CDRs indicate the current state of the observations. The
assimilation method gives a prediction of present population byincorporating the actual appearance of people over time from theconcurrent generated mobile phone activities. We demonstrated
and tested this methodology on the simulation-based system andthe results provide timely and more spatially precise population
estimation in grid square basis. (Abstract)
Keywords- UbiGIS; Data assimilation; Population dynamics;
Urban mobility; Mobile simulationI.

INTRODUCTION

The concept of the daytime population refers to the numberof people who are present in an area during the day or during
normal business hours. This is in contrast to the census datathat represented resident or nighttime population. In 1989,
Stanley K. Smith [9] proposed a methodology to estimate
temporary residents. The limitation of this study is laid on thelack of data sources that provide complete, consistent coverageof temporary movement and migration. Instead, estimates haveto be cobbled together from a variety of administrative records,
business statistics, and sample surveys. There have been otherconsiderable studies done to understand human mobility andimprove the spatial resolution of static population counts
through the use of census data [1][3]. They introduced a projectcall “LandScan”, a population distribution model created by
Oak Ridge National Laboratory, that seeks to overcome thelimitations of aggregated and static population counts by
estimating high spatial resolution population distribution forboth day and night. The project suggested a multi-dimensional
dasymetric modeling approach, which has allowed the creationof a high-resolution population distribution data over the area.
However knowing the average day time population is
insufficient in many particular cases since spatial distributionof people changes over time dependent on the activities andevents that occurred at various times of the day.

II.

MOBILE PHONES AS NEW URBAN SENSORSIn recent years, the large deployment of mobile andwireless technologies has provided new means to understandthe dynamics of a city. The footprint of mobile phone use isoverwhelmingly, and has a side incredible benefit. It istherefore not surprising that considerable research has gone inthis new and interesting directions, in particular, developing
techniques to bring out and explore large scale human mobility[2][4][5]. The previous research [7] has show that how erlang
data, which is a measurement unit of a telecommunication
network, can provide an important new way of looking at thecity as a holistic, dynamic system. This approach cancomplement traditional data collection techniques, which areoften lack of present information and outdated. Other studies
have suggested that erlang measured at particular cells seem tobe a decent indicator of actual presence of people [6][8].
However, without additional information, erlang data itself
could not yield the existing of people who are not using themobile phone. A recent study [10] Song used cell phone billing
data of 50,000 people in a European country to show thatpeople's travel patterns are extremely predictable. He foundthat most people travel very little on a daily basis, for instance,
5 to 10 kilometers or so. There were a few individuals, who ona daily basis travel hundreds of kilometers. This finding
suggests that, for the vast majority of the people, there is anaverage of 93 percent predictability across the user base. It isalso means that by analyzing the amount of mobile phone
footprints, we could possible predict the existence of people
and their commuting path in a daily basis. In contrast withother work in utilizing mobile phone data for human mobility
research, this work is focusing on measuring spatial
redistribution of population over the metropolitan area on a 24hour cycle.
REFERENCES
[1]
[2]

[3]

Bhaduri, B., E. Bright, P. Coleman, and J. Dobson. (2002) LandScan:
Locating People is What Matters, Geoinformatics Vol. 5, No. 2, 34-37.
Candia, J., Gonzalez, M.C., Wang, P., Schoenharl, T., Madey, G.,
Barabasi. (2008) A.: Uncovering individual and collective humandynamics from mobile phone records. Journal of Physics A:
Mathematical and Theoretical Vol. 41(22), 1–16
Dobson, J. E., E. A. Bright, P. R. Coleman, R.C. Durfee, AND B. A.
Worley (2000) LandScan: A global population database for estimating
populations at risk. Photogrammetric Engineering & Remote Sensing
Vol. 66(7), 849-857

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67

[4]

[5]
[6]

[7]

Eagle, N., Pentland, A., Lazer, D. (2009) Inferring social network
structure using mobile phone data. Proceedings of the National
Academy of Sciences (PNAS) Vol. 106(36), 15274-15278.
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L. (2008) Understanding
individual human mobility patterns. Nature Vol. 453(7196), 779–782
Horanont, T., Shibasaki, R. (2008) An Implementation of Mobile
Sensing For Large-Scale Urban Monitoring, UrbanSense08, 4 November
2008.
Ratti, C. (2007). Go with the flow. The Economist Technology
Quarterly, March, 12-13[8]

Reades, J., Calabrese, F., Sevtsuk, A., and Ratti, C. (2007) Cellular
Census: Explorations in Urban Data Collection, IEEE Pervasive
Computing, Vol. 6(3), 30-38[9] Smith, S.K. (1989) Toward a Methodology for Estimating Temporary
Residents, Journal of the American Statistical Association, Vol. 84, 430436.
[10] Song, C., Qu, Z., Blumm, N., Barabsi, A.L. (2010) Limits ofpredictability in human mobility. Science, Vol. 327(5968), 1018–1021.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

68Modelling City Population Dynamics
from Cell Phone Usage Data Streams
Christian Kaiser and Alexei Pozdnoukhov
National Centre for Geocomputation
National University of Ireland Maynooth{christian.kaiser,alexei.pozdnoukhov}@nuim.ie
http://ncg.nuim.ie/i2maps/

Telecommunication systems with their high penetration into modern society provide huge volumes of streaming data on human activities. Data streams are often
aggregated and referenced to an areal spatial unit such as a polygon rather than
to a precise point in space. This is the case when geo-referencing is done by user
IP addresses or from a mobile phone cell ID covering some geographical area, ordata are initially aggregated within an area and attributed to an extended region due to privacy issues. In these cases it is important to adapt the processing
methods to correctly account for an extended spatial support. A typical example
is a spatial interpolation problem when a continuous surface (Figure 1, right)
has to be produced from areal data (Figure 1, left) for downscaling, data homogenization and interoperability in further processing, or simply for visualization.
Many recent applications have demonstrated interpolated human activity heatmaps from areal data such as mobile phone cells [4], however, delt with suchdata in an ad-hoc way with no regard to its areal support at interpolation step.
It limits the usefulness of such maps for rigorous high-fidelity spatial analysis
and decision making as intra-cell areal aggregates and inter-cell flows are notpreserved.

Fig. 1: An example of the area-to-point regression: people density interpolationfrom data aggregated over cell polygons.
Spatial statistics offers various approaches to overcome this problem in part,
including the so-called pycnophylactic interpolation [5] and area-to-point kriging [3]. Surprisingly, there is a theoretical framework that can provide similarfunctionality for a wider class of methods in machine learning, and kernel methods in particular. It is known as the Vicinal Risk Minimisation principle [6].
We develop this framework for spatial data, derive analytical expressions for

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

69Gaussian-like vicinities and introduce an efficient numerical integration schemethat can be used for performing various learning tasks with arbitrary kernels andpolygonal units. The framework is generally applicable to various content-richdata types such as user-generated geo-referenced texts or images.
Particularly, we implement these ideas as an incremental learning algorithm
based on kernel reqursive least squares (KRLS) [1] and apply the derived methods on streaming data. We further enhance the scalability of the method witha MapReduce-based distributed implementation [2] available as an open source
project.
Population density sensing. Population dynamics data are available in various forms of spatial aggregates streaming from fixed hardware installations.
In case the polygonal spatial areas within which streaming data is collected
are known, one can define density p(x|xi , ri ) within these polygons dependingon the type of a measurement system at hand: (1) p(x|xi , ri ) ∼ const corresponding to simple aggregation over the polygon area, or: (2) p(x|xi , ri ) ∼ 1/Si
corresponding to intensity-type measurements, and modulate it according to thespatial alighnment of physical infrastructures within a cell. We used the OpenStreetMap geometries to compute prior occupancy probabilities and enhancespatial fidelity of population estimates. An animation illustrating the temporal
dynamics of the estimates from streaming data is available online1 .

Fig. 2: Processing time with respect to the number of entries in a dictionary ℓ
and a number of samples processed in a stream.

Scaling properties and wide-area deployment. We used an implementation introduced in [2] to investigate the properties of the method when applied tohigh volume stream processing. It operates with a distributed ensemble of kernel
predictors each trained incrementally and stored at local nodes. The implementation is built using the MapReduce framework. Two baseline scaling properties
we would like to demonstrate (see Figure 2) is a constant processing time persample in a stream and a quadratic growth of time with respect to the numberof kernels used in a distributed kernel dictionary.
We also report on experience gained in our current work of implementing
this approach in a real-life setting using a dataset of CDR mobile phone records
1

http://www.youtube.com/watch?v=NJB5Cv_WfMM

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

70for wide area real-time population estimates in Greater Dublin region in Ireland.
We are performing spatial data pre-procesing for parallel kernel computations
over units defined by cell tower geometries using the computational facilities of
the Irish Centre for High-End Computing. The implementation of a streaming
component on mobile phone data records runs locally on a conventional midrange 2.44GHz 8-core server.
Conclusions. Geospatial data analysis often requires dealing with data available at different supports. While spatial statistics provide suitable tools to approach this problem, state-of-the-art applications nevertheless often deal withsuch data in an ad-hoc way. For example, this concerns non-intrusive population density sensing by leveraging data streams from existing telecommunication
infrastructures. The knowledge of physical environment and various constraints
available from high-resolution spatial databases is often either neglected or incorporated at the post-processing stage via simple thresholding.
We introduced a mathematically rigorous and consistent framework to incorporate this knowledge into spatial modelling. This framework is applicablefor a broad class of machine learning algorithms. We derived a particular areato-point regression method and adapted an incremental training algorithm toapply it for streaming data. The validity of the method for real-time sensing ofcity population density from a typical data stream available from telecommunication infrastructures was demonstrated. We investigated the scaling propertiesof the algorithm and found it to be promising approach to take in our current
work which extends it into a larger-scale system useful to uncover high-fidelity
patterns of city dynamics.
Acknowledgments. Research presented in this paper was funded in part throughStokes Lectureship programme and Strategic Research Cluster grant (07/SRC/
I1168) by Science Foundation Ireland, and faculty research awards from Google
and IBM. The authors gratefully acknowledge this support.

References
1. Engel, Y., Mannor, S., Meir, R.: The kernel recursive least-squares algorithm. IEEE
Transactions on Signal Processing 52(8), 2275–2285 (2004)
2. Kaiser, C., Pozdnoukhov, A.: Enabling real-time city sensing with kernel stream
oracles and mapreduce (June 2011), the First Workshop on Pervasive Urban Applications (PURBA)
3. Kyriakidis, P.C.: A geostatistical framework for Area-to-Point spatial interpolation.
Geographical Analysis 36(3), 259–289 (2004)
4. Ratti, C., Pulselli, R., Williams, S., Frenchman, D.: Mobile landscapes: Using location data from cell phones for urban analysis. Environment and Planning B 5(33),
727–748 (2006)
5. Tobler, W.: Smooth pychnophylactic interpolation for geographical regions. Journalof the American Statistical Association 367(74), 519–530 (1979)
6. Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience (September 1998)

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71Exploiting cellular network MSC counters for the analysisof temporary populations
Paolo Tagliolato, Fabio Manfredini, Carmelo Di Rosa

Dipartimento di Architettura e Pianificazione, Politecnico di Milano
Via Bonardi, 9 - 20133 Milano, Italy{paolo.tagliolato, fabio.manfredini, carmelo.dirosa}@polimi.it

Introduction

Mobile switching center data

Visitors and tourists are among the more important urban
populations for their impact on local economy, on global
fluxes dimensions and on urban vitality and attractiveness.
Despite the fact that this assumption is widely sharedwithin the urban studies community, there is a lack of available and real-time data on such significant dimensions ofcontemporary cities. Tourism statistics contain data about
the activity of registered accommodation establishments,
hotels, motels, hostels, youth hostels, holiday villages andcamping sites. Data are collected monthly, electronically orwith questionnaires or as computer printouts from the accommodation establishments within the scope of the statistics.
Urban attractiveness is moreover characterized by otherforms of tourism and visitors, which are not adequately
taken into account by traditional surveys and statistics, e.g.
the huge amount of people attracted by cities for every
reason and moving beyond the boundary of oﬃcial channels (black market accommodation, hospitality from friends,
couch-surfing, etc.).
In recent years we assisted at an extraordinary increasein mobile communications, a phenomenon referred to as thenext social revolution [7]. In this scenario, mobile phoneis the widest adopted technology and, according to several
studies, it is changing the way people organizes its daily
life [3][5]. New ICT oﬀers users new time-spatial coordination tools and it influences human activity-travel behavior, tending to increase people’s spatial mobility [8] [4], andenabling people to carry on several activities while on themove [6]. Mobile phone traﬃc data are promising sources forlarge scale surveys due to the high pervasiveness of cellular
phones in contemporary societies [1]: users provide information about the (use of the) territories by simply using
a technology that is more and more ubiquitous, or, in thewords of Weiser [9], a technology that disappeared ”woven
into the fabric of everyday life”. Moreover, these data areautomatically generated from the telecommunication networks, so that there is no need for huge investment such as
those necessary for the acquisition of traditional data banks.
In this paper we will introduce and exploit a particularkind of cellular network data, namely HLR MSC counters,
novel in literature at our knowledge. What could be thecontribute of MSC data to extend conventional sources was
the core topic of our research.

We had the opportunity to use data about switched-on mobile phone rather than on mobile phone traﬃc (calls received
or made). The data refer to the mobile switching center(MSC), which is the primary service delivery node for GSM,
responsible for routing voice calls and SMS. It also records
information on the mobility of subscribers by updating theposition of mobile devices in the HLR (Home Location Register), which contains all subscriber information.

NetMob 2011 MIT (Media Lab), Cambridge, MA, October 10-11, 2011

Figure 1: Map of the Lombardia region’s MSC Service Area and patterns of registered users
The service area of each MSC has a variable size depending on the number of GSM tower cells served and on theintensity of mobile phone traﬃc generated inside it. MSC
service areas are small in dense urban areas and they can bevery large in suburban areas (even thousands of square kilometers). Despite their diﬀerent sizes, the spatial distributionof the MSC serving areas appears to be very interesting interms of urban dynamics and land use.1
The data at our disposal were, for each MSC service areaof the Lombardia region , the number of registered users (i.e.
having the phone turned on and logged on TIM’s network)
distinguished by the nationality of the SIM at a hourly base.
The interest in this type of information, despite of the low
spatial resolution, is due to the possibility to have access to
information directly related to the number of active phones,
which may correspond approximately to hourly people presence in the diﬀerent MSC service area.
The further possibility to access information on the na1
For example: the central area of Milan is divided into two MSC
service areas; other important cities like Bergamo and Brescia aredefined by a single MSC service area; the sparse and mountainous
northern side of the region is also divided in two big MSC service
areas; the dynamic northern side of Milan is shared by 3 MSC
service areas.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

72tionality of the SIM of active customers has also suggested
to make an assessment on the potential of this data for monitoring foreign presences in diﬀerent MSC.

Data analysis
We carried out some analysis on the variability of the number of active users in each MSC for two periods of time
(September 7 20, 2009; April 1 30, 2010). We tried to determine whether and how these data might provide new anduseful understanding of urban temporary population dynamics at diﬀerent temporal scales.We considered the density of telephone contacts (active customers per square km),in order to take into account the substantial diﬀerences insize between the MSC, and performed several analyses on
its variation over time in the MSC service areas of the Lombardia region .

The graph highlights diﬀerent temporal patterns of daily
population:• huge decrease of active clients during the weekend due
to reduction of job and study travels• characteristic inflection in the Eastern period. Weekend April 4 5 more pronounced than that of other
April weekends (inhabitants of Milan leave the city)
The possibility to obtain an indirect measure of the temporal variation of presences in the city is a great value of
this data, relevant for policy and decision makers.

Monitoring urban populations
The variability of active clients during working days highlights, for the urban dense Milan MSC service areas, theattractive role of the city for jobs and services. In fact, during working days, the city of Milan attracts a large amountof people from a vast territory that goes beyond the municipal boundaries, namely Milan Urban region. In otherterms, this phenomenon can be referred to the daily mobility and its dimension. We show (figure 2) this result bycomparing, at an hourly basis, the amount of active clientsduring a Wednesday and during a Saturday in two areas.
We observe that the ratio Wednesday / Saturday sharply
increases in Milan during the first hours of the day until 8
AM (marked in black). Afterwards, the amount of contacts
is steady. After 4 PM the number of active clients decreases
very slowly, until it reaches the same value of the morning.
The number of contacts during Wednesday is more than ahalf than that of Saturday. Milan municipality’s oﬃcial data
inform that each working day almost 600.000 people enterin Milan, a value that is compatible with what emerges from
MSC service area statistics.
The dotted line refers to a mountainous MSC service areain the Northern Lombardy region, This area is characterized
by a dependent profile in terms of people attractiveness during the working days, compatible with the activities present
in the area.

Figure 2: Hourly Active clients: working day / Saturday. Service area central Milan (7-20/9/2009)
The monthly Milan city rhythm is shown in figure 3, which
represents the hourly variation of active clients in one MSC
service area for the period April, 1st 2010 April, 30th 2010.

Figure 3: Active clients density in the MSC service
area of Milan (1-30/4/2010)

Foreigners trends and evidences
MSC counters register the nationality of the SIM connectedto the system. We tried to determine the potential of thisinformation for monitoring tourists and visitors, studying
their variability in Lombardia Region MSC service areas.
Nowadays, tourism statistics are related to capacity andoccupancy in collective tourist accommodation, and they
are collected via surveys filled in by accommodation establishments. Statistics on the occupancy of collective tourist
accommodation refer to the number of arrivals (at accommodation establishments) and the number of nights spent
by residents and non-residents, separated into establishment
type or region; annual and monthly statistical series areavailable.
This information is available at the provincial spatial scale
and underestimates the dimension of phenomena, because
it considers only tourists or visitors who go to oﬃcial structures. A large proportion of temporary visitors are therefore excluded. It is estimated that in Milan there are about
500,000 visitors per year who are not counted by statistics
because they spend nights by friends or by informal touristic
structures (Observatory of Tourism - Milan).
On the other hand, a strong need to know the dimensionof tourism is expressed by several urban stakeholders, suchas municipalities, public agencies, industry and trade organizations: they are interested to increase the attractivenessof urban regions, providing new services and activities targeted for this type of temporary population (tourists, business people, etc.).
We focused on the most numerous nationalities registeredin MSC in September ’09. The first, German (23%), wasmostly concentrated in the big MSC service area covering
the western side of the Lombardia plain and the Garda lake
(fig. 4), one of the main touristic attractor in inland Northern Italy, in particular for German population. The trend
during September 2009 is clearly due to the presence of German tourists in the Garda Lake area for the summer.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

73ties, during the week of the event, the most represented ones
were France, Switzerland, United Kingdom, Spain, German,
Russian Federation and USA, accounting for more than the
50% of the total foreigners.
The spikes of attendances are during Thursday and Friday
for all the nationalities except for the Swiss, which had its
peak on Sunday.
Figure 4: German active clients in MSC service areaof Lombardia region - Sept.2009
The National Statistic survey on Tourism indicates, in
September 2008, in the province of Brescia, the presence ofabout 915,000 tourists, most of which (82%) were concentrated in the lake and the mountain areas, entirely included
in one MSC service area. In the same year, Germans scoredmore than 2,000,000 admissions, representing 41% of total
foreign tourists and 25% of the total. Although the two data
sources diﬀer in level of detail, period, nature of the data, it
seems that the data on the variability of active clients MSC
can eﬀectively describe the dynamics of tourist arrivals.
Swiss visitors (15%) have a rather diﬀerent trend. Their
distribution is indeed more concentrated in the MSC area
near the border between Switzerland and Italy, in the northern side of the region and it is characterized by a positive
peak on Saturdays and Sundays.
Americans are mainly concentrated in the center of Milanand show a growing trend on working days, from Monday
onwards, and a subsequent decrease in the weekend, a phenomenon that seems to be compatible with business travel
rather than tourism travel.

Conclusions
Despite of the dimension of the MSC service area, the data
were able to catch the foreign trends as a phenomenon visible
at a urban scale. For major events, active clients can provide an important tool for monitoring visitors and tourists,
for evaluating the attractiveness of a big event, for evaluating its economic impact and for providing new touristic
and business services directed to specific nationalities. On
these topics there is a strong need of updated knowledge
on urban attractiveness, a major component of global cities
competition which is hardly intercepted by standard surveys. Innovative methods are therefore required for measuring and monitoring tourists and visitors at diﬀerent urban
scales. MSC active clients data seems to be promising, providing informations that currently institutional sources do
not oﬀer.

Acknowledgments
The authors would like to acknowledge Piero Lovisolo, Dario
Parata and Massimo Colonna, Tilab - Telecom Italia for
their collaboration during the research project. This work
was supported by Telecom Italia.

Monitoring visitors during a big event

References

We performed specific investigations regarding the period of
the Milan International Design Week (April 14 19, 2010),
a leading event that takes place in the Rho-Pero Exhibition
District and in dozens of places inside the city where art galleries and museums, shops, industrial areas and showrooms
host events and exhibitions, parties and special initiatives(named ”Fuorisalone”).
For a better comprehension of foreign dynamics, Italians
were excluded from the analysis. The foreign active clients
present a trend that is very related with the International
Design Week development. In fact, it is immediate to observe that since the day before the beginning of the Event(April 13), the number of foreigners increases sharply until
it achieves its peak on Saturday April, 17. Afterwards thecurve decreases slowly and reaches a more typical course.

[1] N. Caceres, J. Wideberg, and F. Benitez. Deriving
origin destination data from a mobile phone network.
Intelligent Transport Systems, IET, 1(1):15–26, March
2007.
[2] M. Goodchild. Citizens as sensors: the world of
volunteered geography. GeoJournal, 69:211–221, 2007.
10.1007/s10708-007-9111-y.
[3] J. Katz. Connections: Social and cultural studies of thetelephone in American life. Transaction Pub, 2003.
[4] M. Kwan. Mobile communications, social networks, andurban travel: Hypertext as a new metaphor forconceptualizing spatial interaction*. The Professional
Geographer, 59(4):434–446, 2007.
[5] R. Ling. The mobile connection: The cell phone’s
impact on society. Morgan Kaufmann Pub, 2004.
[6] G. Lyons and J. Urry. Travel time use in theinformation age. Transportation Research Part A:
Policy and Practice, 39(2-3):257–276, 2005.
[7] H. Rheingold. Smart mobs: the next social revolution.
Basic Books, Cambridge, MA, 2002.
[8] A. M. Townsend. Life in the Real-Time City: Mobile
Telephones and Urban Metabolism. Journal of Urban
Technology, 7(2):85–104, 2000.
[9] M. Weiser. The computer for the 21st century.
SIGMOBILE Mob. Comput. Commun. Rev., 3:3–11,
July 1999.

Figure 5: Foreign active clients density in the MSC
service area of Milan - The International Design
Week (April 14 to 19, 2010).
Among the more than one hundred registered nationali-

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74

Mobility and Predictability of Population Movements after the Haiti 2010
Earthquake
Xin Lu¹²*, Linus Bengtsson¹†, Petter Holme³²‡¹Department of Public Health Sciences, Karolinska Institutet, 17177 Stockholm, Sweden²Department of Sociology, Stockholm University, 10961 Stockholm, Sweden³IceLab, Department of Physics, UmeåUniversity, 90187 Umeå,Sweden*†‡
Emails: *lu.xin@sociology.su.se; †linus.bengtsson@ki.se; ‡petter.holme@physics.umu.se.

Abstract:
In 2010, 385 natural disasters caused up to 300,000 deaths worldwide, affected over 217 million
and resulted in US$ 120 billions worth of damage. Large population movements are a common
consequence of large-scale disasters and itself a cause of secondary problems. To understand andpredict the movement of people in disasters is one of the keys to effective humanitarian relief aswell as long-term societal recovery.
We analyzed the movements of 2.9 million people during the period 42 days before to 342 days
after the tragic Haiti earthquake of January 12, 2010. While the earthquake caused 25% of themobile phone users in the earthquake-affected capital of Port-au-Prince to leave the capital 19 days
after the earthquake the predictability of movements over the longer term remained high and theentropy of movements returned to the pre-quake patterns already three months after theearthquake. On the other hand, the time to return to Port-au-Prince after having fled the disasterhas a heavy-tailed distribution, illustrating how heterogeneously affected individuals are. The
destinations of the large number of phones that left the capital within the first 19 days of theearthquake were highly correlated with their pre-earthquake movements. Specifically, 52% of thedisplaced Port-au-Princians also left the capital during the preceding Christmas holiday and out ofthese 85% returned to the same province as during Christmas.
Studying entropy-based predictability measures, we find that the travel pattern of the affected, arehighly predictable. This means that historical trajectories can be used to predict the movement in adisaster, to aid decision makers. These analyses also show that, as a fast, convenient method, thetracking of mobile phones can be used as an emergency response system to provide useful
information during nature disasters.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

75Anonymizing Location Data Does Not Work

1.

Hui Zang

Jean Bolot∗

SprintBurlingame, CA 94010, USA

Technicolor
Palo Alto, CA 94301, USA

hui.zang@sprint.com

jean.bolot@technicolor.com

INTRODUCTION

We examine a very large-scale data set of more than 30
billion call records made by 25 million cell phone users acrossall 50 states of the US and attempt to determine to what
extent anonymized location data can reveal private user information. Our approach is to infer, from the call records,
the “top N ” locations for each user and correlate this information with publicly-available side information such as
census data. For example, the measured “top 2” locations
likely correspond to home and work locations, the “top 3” tohome, work, and shopping/school/commute path locations.
We consider the cases where those “top N ” locations aremeasured with different levels of granularity, ranging froma cell sector to whole cell, zip code, city, county and state.
We then compute the anonymity set, namely the number ofusers uniquely identified by a given set of “top N ” locations
at different granularity levels.
We find that the “top 1” location does not typically yieldsmall anonymity sets. However, the top 2 and top 3 locations do, certainly at the sector or cell-level granularity. We
consider a variety of factors that might impact the size of theanonymity set, for example the distance between the “topN ” locations or the geographic environment (rural vs urban).
We also examine to what extent specific side information, inparticular the size of the user’s social network, decrease theanonymity set and therefore increase risks to privacy.
Our study shows that sharing anonymized location data
will likely lead to privacy risks and that, at a minimum,
the data needs to be coarse in either the time domain(meaning the data is collected over short periods of time,in which case inferring the top N locations reliably is difficult) or the space domain (meaning the data granularity is strictly higher than the cell level). In both cases, theutility of the anonymized location data will be decreased,
potentially by a significant amount.
A more complete report of this study is published as [1].

2.

LOCATION PRIVACY

Call Data Records (CDRs) contain information of every
call carried by the cellular network, including time, location, and identities of both parties involved in the call. There∗

Part of this work was done while the author was working at Sprint.

Figure 1: Median and mean size of anonymity set vs. distance between top locations

is an increasing need or desire to share CDRs, or location
traces in general, to third parties and the standard approachis to anonymize those traces, which raises important issuesof privacy which can be summed up in the question: Is it
safe to share these anonymized CDRs or location traces?
A privacy breach occurs when users are re-identified fromanonymized data. Earlier work has shown that a fractionof the US working population can be uniquely identified bytheir home and work locations, even when those locations
are not known accurately. Given that the top locations visitedby a mobile user often correspond to the home and work locations, the risk in releasing locations traces of mobile phoneusers appears very high.
We use a well-known metric, k-anonymity to quantifies
the degree of privacy of an anonymized data set. With kanonymity, each individual will be in distinguishable fromat least k − 1 others, i.e., is “hiding in crowd of k”. Whenapplied to location traces, k-anonymity means that the mobility behavior of a user is similar to that of at least k − 1other users. Equivalently, k is the size of what we refer to as
the anonymity set.
We study a data set of 30 billion CDRs from a nationwide
cellular service provider in the United States which contains
location information of about 25 million mobile phone users
over a three-month period. We examine important factorsthat affects the privacy of users when their anonymized “topN ” locations are shared, in particular:• The value of N : Tables 1 through 3 summarize the 1st

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76

(a) Illinois(b) Missouri(c) California

Figure 2: k-anonymity for people whose top location is in the specific states, in the specific cities and the rest of the state.

(a) Cell(b) City(c) County(d) State

Figure 3: Size anonymity set on different granularity levels for people whose top location is in the specific state: California (CA), New York (NY), Texas (TX), and Illinois (IL).

percentile, the 5th percentile, the 10th percentile andthe median of the k-anonymity values at six granularity
levels: sector, cell, zip-code, city, county, and state, for
N = 1, 2, 3, respectively. Entries in the tables witha “1” indicate the fraction of users that are uniquely
identified. We can see that the larger N is, the smaller
the anonymity sets are. In other words, one is more
identifiable when more top locations are known.
• The location granularity: As shown by Tables 1 through3, the number of individuals that can be re-identified
decreases as the top N locations are known with increasingly coarse granularity, i.e. vary from the sector
to the state level. For example, according to Table 2, atthe “sector” and “cell” levels, between 10% and 50%of the users are uniquely identifiable; at the “zip code”
level, 5% and at the “city” level, 1%. In other words,
if we know the top two locations for each user at the
granularity of a cell or of a city, and given that the total
population under study is 20 million, we can uniquely
identify 2,000,000 users (cell level) and 200,000 users(city level), respectively.
• The distance between locations: As shown in Fig. 1,
the longer distance between the top two locations, thesmaller the anonymity set. The figure also shows thatthe size of the anonymity set decreases approximately
inversely to the distance between the two locations.

• The geographical regions: We find that different geographical areas have different levels of privacy risks,
and at a different granularity level this risk may behigher or lower than in other areas. We investigate
users whose top location is in selected states or in/outof specific cities. The results are shown in Figs. 2 and3. In Figure 2(a) for example, users in Chicago havemuch smaller anonymity sets than users in the rest of
Illinois. This observation suggests a difference in kanonymity linked to urban/rural lifestyles: in urban
environments, we expect users’ lifestyles to be morediversified and hence the anonymity sets to be smaller.
Fig. 2(b) shows that users in St. Louis are quite different from the those in the other areas of Missorri,
whereas users in Kansas City fall somewhere in the
middle. In California (Fig. 2(c)), the anonymity setsof users in San Francisco and Los Angeles are similar. The rest of California is less at re-identification
risk than these two cities. Figure 3 shows the distribution of k-anonymity for users in the four statesat the cell, city, county, and state levels, respectively.
One state may have higher anonymity (larger k) at one
level, but lower anonymity (smaller k) at another level.
For example, users in California have by far the highest anonymity at the county level (Fig. 3(c)), but havethe lowest anonymity at the cell level (Fig. 3(a)). Users

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

77

Table 1: k-anonymity with top 1 location
Location
Size of anonymity set (k)granularity 1st %ile 5th %ile 10th %ile Median
Sector
28
71
111
372
Cell
92
220
331
967
Zip code
184
557
909
3125
City
162
487
874
7638
County
802
2972
6272
55649
State
60139
1.5e+05 2.6e+05 7.2e+05(a) CellTable 2: k-anonymity with top 2 locations
Location
Size of anonymity set (k)granularity 1st %ile 5th %ile 10th %ile Median
Sector
1
1
1
2
Cell
1
1
1
9
Zip code
1
1
2
75
City
1
2
6
437
County
2
23
143
15628
State
530
6912
51291
6.8e+05
Table 3: k-anonymity with top 3 locations
Location
Size of anonymity set (k)granularity 1st %tile 5th %tile 10th %tile Median
Sector
1
1
1
1
Cell
1
1
1
1
Zip code
1
1
1
2
City
1
1
1
24
County
1
2
7
3407
State
40
1074
5671
4.6e+05

in Texas have higher anonymity than the other states atthe cell level, but lower anonymity than the others at
the county level.

(b) State

Figure 4: k-anonymity with additional social information and when quasi-identifier is an unordered pair oflocations.

blue curves denoting the size of anonymity sets withand without this extra bit, respectively. Please referto [1] for impact at other location granularity and formeaning of the green curves.

3.

CONCLUSION

In this paper we conducted a large scale study on the riskof re-identification attacks with published location data obtained through call records. Our study shows that publishing or sharing anonymized location data will likely lead toprivacy risks and that, at a minimum, the data needs to becoarse in either the time domain (meaning the data is collected over short periods of time of the order of a day, inwhich case inferring the top N locations reliably is difficult)or the space domain (meaning the data granularity is strictly
higher than the cell level). In both cases, the utility of theanonymized location data will likely be decreased by a significant amount.

• Additional knowledge about social behavior: We consider the case when more information is released about
users than just location information, in particular information about the size of their social network (forexample measured by the number of unique individuals they call during a month) and speficially whether
the network is large or small. We use 20 as the threshold size between large and small (the reason for 20 is
explained in the reference below) and we introduce anextra bit of information to the top N locations indicating the size of an individual’s social network rela4. REFERENCES
tive to this threshold (i.e. whether it is above or below20). We observe that with this extra bit of informa- [1] H. Zang and J. Bolot, “Anonymization of location data doestion, the size of anonymity sets drops about 50% at
not work: A large-scale measurement study”, Proc. ACM
every granularity level, which indicates that social beMobicom 2011, Las Vegas, NV, Sept. 2011 (to appear).
havior is usually orthogonal to mobility behavior andthat additional knowledge about the users’ social patterns is helpful to re-identify those users. We demonstrate this impact in the case of top 2 locations, at celland state granularity, in Fig. 4 with the red curves andNetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

78

Using Randomization Methods to Identify Social Influence in
Mobile Networks
Rodrigo Belo∗, Pedro Ferreira†Carnegie Mellon University
July 15, 2011

1

Introduction

Identification of social influence in observational data is not trivial; endogeneity issues such as homophily,correlated unobservables, or simultaneity, that frequently pose challenges to the researcher interested inestimating the magnitude of a causal relation in the diffusion process (Shalizi and Thomas, 2010). Commonlyused identification strategies include the definition of structural models (e.g., Ma et al., 2009), the use of
instrumental variables (e.g., Tucker, 2008), propensity score matching (e.g., Aral et al., 2009), and more
recently, randomization (e.g., Anagnostopoulos et al., 2008; La Fond and Neville, 2010).
Randomization techniques consist of generating pseudo-samples based on the original sample by selectively permuting the values of some variables among observations (Noreen, 1989), allowing for the estimationof empirical distributions of a parameter of interest under the null hypothesis (e.g., Anagnostopoulos et al.,2008; La Fond and Neville, 2010). We apply these methods to assess the magnitude of peer influence in the
adoption of products and services in a mobile network setting.
We use a comprehensive panel of data from a large European mobile network provider. The data arecomprised of detailed information about all the subscribers, including call and SMS detail records, pricing
plans, products and promotion adoptions, and handset information over an 11-month period. We estimate
the influence effect in a carefully selected set of products from a total of 1200 supplementary services offeredby the network provider.
We provide preliminary evidence for the existence of positive and negative social influence in the adoption
process, depending on the products analyzed, and we aim to explore how social influence is affected byproduct characteristics.

2

Randomization methods to identify peer influence in social networks

Randomization tests are a technique that have been used for non-parametric hypothesis testing based on
permutations of values among observations (Noreen, 1989). The key idea is that under the null hypothesis
these permutations correspond only to random disturbances in the data and should not alter the statistics
we’re interested in estimating. The test is conducted as follows. The original data is altered several times by
permuting some attributes among individuals, each permutation originating a pseudo-sample. A test scoreis calculated for each pseudo-sample, and from these test scores an empirical distribution is estimated. The
test score of the original data is compared to the estimated distribution, and its significance is assessed. Tworecent studies outline randomization strategies to identify peer influence effects when information about thetiming of actions is available.
Anagnostopoulos et al. (2008) propose a test to identify influence as a source of correlation in a social
network, the shuffle test. This tests consists of shuffling the adoption date among the edges that eventually∗ Rodrigo Belo, CMU, rbelo@cmu.edu.
† Pedro Ferreira, CMU, pedrof@cmu.edu.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

79will become adopters. The test is based on the idea that under the no-influence null hypothesis, adoption
dates should be independent, and therefore the obtained correlation coefficient should be the same whether
or not adoption dates have been shuffled. In the case the original correlation coefficient is different from the
shuffled one, the null hypothesis can be rejected and we can conclude we are in the presence of influence.
La Fond and Neville (2010) describe a general randomization method to identify homophily and influencein a two-period setting. They define a correlation measure that increases with the the number of connectededges with similar attribute values and with the number of unconnected edges with different attribute values.
They calculate empirical distributions for this auto-correlation statistic under the null hypothesis that there
is no influence (or homophily). Finally, they compare the statistic obtained from the actual change with theempirical distribution and determine whether the null hypothesis can be rejected or not.
Notably, their randomization strategy is not suitable in the case of adoption of a single product in which
the only relevant attribute change is binary. The shuffle test proposed by Anagnostopoulos et al. (2008) issuitable for the adoption case, but requires a longitudinal view of the data, i.e., when performing the analysis
the researcher needs to know exactly which individuals end up adopting the product, so that the shuffling
can be performed among all eventual adopters.
In sum, the application of randomization methods to identify influence in social networks is still in an
emergent stage and needs further research. We set out to explore these methods and develop a deeper
understanding on their potential and limitations.

3

Data

We have secured access to a 11-month anonymized panel of data comprising of detailed information about
all subscribers in a large mobile European network provider. The data includes detailed information on
demographics (age, gender and zip code), call and SMS data records, pricing plans, product and promotion
adoptions, as well as on subscribers’ handsets.
The data are comprised of detailed information about every call and SMS originated and received byroughly 4 million subscribers during the period of analysis. These details include, among others, origin,destination, start time and duration of a call. On an average day subscribers generate about 4 million
calls and exchange 40 million SMSs. Additionally, the data contain information about subscribers’ pricing
plans and supplementary services. At a given moment in time each subscriber is associated with one pricing
plan, and possibly several supplementary services. Supplementary services are à la carte add-on services
that subscribers can acquire, and that can be virtually anything the network operator decides, such as apack of 1000 SMSs at a discounted rate, free calls on the weekends for a given period of time, or simply
voice-mail activation. The operator has more than 1,200 distinct services, and there are a total of 10 million
supplementary services subscriptions during the period of analysis, which corresponds to an average of 2.5
services per subscriber.
We estimate influence effects on a carefully selected set of services. Follows a brief description of three ofthem that have been analyzed so far: (1) a one time promotion in which subscribers get their phone accountcredited by the amount corresponding to the minutes they have spent during the month of December. To
benefit from this promotion subscribers must pay a fixed entry fee; (2) an ongoing promotion in which
subscribers pay a fixed fee to be able to perform video calls at discounted rates; (3) a one time promotion inwhich subscribers can call free at night during the month of July. To benefit from this promotion subscribers
must pay a fixed entry fee.
We summarize and extract information about the network on a monthly basis. We calculate monthly
summaries for each subscriber, including number of call neighbors, SMS neighbors, total number of calls
made and received, and total conversation time. Also, in a first stage, we limit our analysis to a sample of
10,000 randomly selected subscribers and their direct neighbors.

4

Methods

We follow Anagnostopoulos et al. (2008) and define our problem as follows. Consider relational data represented as an undirected time-changing graph, G = (V, E), where V is a set of nodes and E a set of undirectededges, eij , each edge connecting two nodes. Each node v ∈ V has a time-changing attribute Wvt indicating
whether the node has adopted the product at time t or before.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

80Additionally, assume that at each time period, t, the probability of a node to adopt a given productfollows a distributionthat depends on the number of connected nodes v 0 that have already adopted thePv
product, ait = vj :eij ∈E Wt j , and on her own characteristics, Xi : p(ait , Xi ).
Due to computational restrictions, and because our interest lies mainly in the first-order marginal effectsof having one more adjacent adopter node, we estimate a linear probability model (LPM):p(ait , Xi ) = αait + Xi β + εit
where α corresponds to our parameter of interest and β is a parameter vector.
As mentioned above, α might be capturing not only the influence effect but also other sources of correlation, such as homophily and unobserved confounding variables. Thus, we estimate an empirical distribution
for α under the null hypothesis of no influence, and then compare the parameter obtained from the original
data with the empirical distribution.
The no influence hypothesis can be stated as follows:
H0 : The probability of node i adopting a given product at time t is not determined by the number ofadjacent nodes that have already adopted, ait .
We generate randomized versions of the data as close as possible to the original, only without assumingthat the time of adoption is important for the diffusion process: we shuffle the time of adoption among
eventual adopters as suggested by Anagnostopoulos et al. (2008). This transformation has the advantage
of preserving the network-level statistics, and thus avoiding potential problems of changing too much theoriginal data. To calculate the empirical distribution we run the LPM model and obtain an estimate of α
for each randomized version of the data. We can reject the null if α in the original data does not fall in the
95% confidence interval of the empirical distribution. Moreover, we identify social influence as the difference
between the average value of the empirical distribution and the coefficient obtained with the original data.

5

Results, Contribution and Challenges

We identify social influence on three products with distinct characteristics. Table 1 shows the total numberof adopters for each of these products (column (a.)), the coefficient obtained from the original data (column(b.)), the average coefficient obtained from randomization and respective standard deviation (column (c.)),and our estimate of social influence (column (d.)). For product (1) the average coefficient obtained fromthe randomization is .0054. Given that the coefficient obtained using the original data is .0064, outside the
95% confidence interval of the empirical distribution (see Figure 1), we conclude that social influence plays
a role in the diffusion of this product. Its total effect corresponds to 5% of the total observed adoption.1
We find no evidence of social influence in product (2), and for product (3) we find a negative effect. This
result might be due to a substitution effect: there is no incentive for an adopter’s contacts to subscribe theproduct since they will benefit from talking with their interlocutor for free anyways, as long as the adopter
is the one who initiates the calls.
The different characteristics of the products analyzed might provide some insight on why we see differences
in magnitude and sign of social influence on mobile network products. We need to further explore this topic byincluding more products in our analysis and by formulating hypotheses about specific product characteristics
that affect social influence.
We characterize peer influence in the context of mobile network services, assess its impact in the diffusion
process and evaluate its importance for marketing-related decisions in this context. We provide preliminary
evidence for the existence of different social influence effects given different product characteristics. We
also shed some light on the advantages and limitations of randomization methods in the identification of
influence and, by applying these methods in a different context, we try to contribute to their consolidation
as a valuable alternative to other identification methods.
1 Given that in our sample each person is exposed to .09 adopters per day on average, the total influence effect corresponds
to 28 extra adopters (from a total of 534 adopters) over the 32 days adoption occurs, which corresponds to 5%.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

81

100
80
Frequency
40
60
20
0
.0045

.005

.0055
.006
Randomization Coefficients

X
.0065

Figure 1: Product (1) coefficients over 1000 adoption date shuffles. The ‘×’ mark represents the coefficientobtained from the original data.

Product(a.)
Adopters

(1)

534

(2)

298

(3)

110

(b.)
Original Data Coeff. (α)
×103

(c.)
Randomization Avg. Coeff.
×103(s.d.)
6.4
5.4
(.29)
.11
.10
(.006).30.60(.075)*** p<0.01, ** p<0.05, * p<0.1(d.)
Influence Est.
×103
1.0***.01-.3***

Table 1: Influence estimates for selected products.

References
A. Anagnostopoulos, R. Kumar, and M. Mahdian. Influence and correlation in social networks. In
KDD ’08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery
and data mining, pages 7–15, New York, NY, USA, 2008. ACM. ISBN 978-1-60558-193-4. doi:
http://doi.acm.org/10.1145/1401890.1401897.
S. Aral, L. Muchnik, and A. Sundararajan. Distinguishing influence-based contagion from homophily-driven
diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51):21544, 2009.
T. La Fond and J. Neville. Randomization tests for distinguishing social influence and homophily effects. In
Proceedings of the 19th international conference on World wide web, pages 601–610. ACM, 2010.
L. Ma, R. Krishnan, and A. Montgomery. Homophily or Influence? An Empirical Analysis of Purchasewithin a Social Network, 2009.
E. Noreen. Computer Intensive Methods for Testing Hypothesis- An Introduction. JOHN WILEY & SONS,(229), 1989.
C. Shalizi and A. Thomas. Homophily and contagion are generically confounded in observational social
network studies. Arxiv preprint arXiv:1004.4704, 2010.
C. Tucker. Identifying formal and informal influence in technology adoption with network externalities.
Management Science, 54(12):2024, 2008.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

82Quantifying and Modelling Good Communicators in Dynamic
Phone Networks
Alexander Mantzaris
alexander.mantzaris@strath.ac.uk

Abstract
Dynamic networks are those with a connectivity
structure that changes over time. In this setting, the ability of a node to transmit or receive
information cannot be accurately measured, ingeneral, by considering only simple snapshots ortime-averages. In this presentation, in the context of cell phone communication, we describe
some recent ideas in (a) measuring node centrality and (b) defining explanatory mathematical models, for dynamic networks. Our focus ison dynamic communicators: individuals who, either through status, intelligence or foresight, areable to communicate extremely effectively, in amanner that could not be discovered from static
centrality measures.

1

Dynamic Centrality

Modelling and analysis of static networks has
a long and distinguished history, and there aremany concepts and tools available [6]. However,∗Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK. This work was supported by the Engineering and Physical Sciences ResearchCouncil and the Research Councils UK Digital Economy
Programme, through the project MOLTEN: Mathematics
Of Large Technological Evolving Networks.

Desmond J. Higham
d.j.higham@strath.ac.uk ∗information about communication networks generally evolves dynamically, and although a static
analysis can proceed by aggregating over time,
we argue here that value can be added by respecting time’s arrow. We will show that a fully
dynamic viewpoint can uncover useful information that is lost with static summaries.
Edges between nodes (participants in the network) can be created by making phone calls,
sending emails, or from any type of exchange of
information that has finite duration. Consider afixed set of N nodes in a network producing dataof the form {A[k] ∈ RN ×N } for k = 0, 1, 2, . . . , M ,representing theadjacency matrix at each time
tk . Hence, A[k] ij = 1 if there is an edge from
node i to node j at time tk and A[k] ij = 0 otherwise. The time points t0 < t1 < · · · < tM areordered but not necessarily equally spaced.
To develop a dynamic measure for the centrality of the nodes, the downstream contribution (knock-on effect) of an edge can be calculated via the concept of dynamic walks, whereedges are traversed in a manner that respects thetime ordering. In [3] it was shown how to compute a matrix Q ∈ RN ×N for which (Q)ij is aweighted count of the number of dynamic walksof length w from node i to node j. Here, lengthis measured as the number of edges crossed. To
model the loss of influence as walk length in-

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

83creases, walks are weighted by a factor αw where0 < α < 1 is a fixed parameter. One intuition for
down-weighting the contribution of longer walks
is that information may become less relevant or
reliable with length. Alternatively, α may be interpreted as the independent probability that amessage successfully traverses an edge. The matrix Q may be written in the form

−1

−1
Q = I − αA[0]. . . I − αA[M ].

30 consecutive day periods are presented. Circled in red in Figure 1 are nodes which rank in
the top 3 in terms of their broadcast centrality
but do not rank within even the top 10 nodes
in terms of their degree. Nodes of this type arenamed dynamic communicators in [5], since astatic measure (in this case, aggregate degree)
fails to allocate high centrality to them despite
their ability to communicate across the network.
3
BROADCAST

In practice, if our aim is to rank nodes, it isequivalent and computationally safer to iteratewith the normalized form
−1Q̂[k−1] I − αA[k]
[k]
Q̂ =
−1 .
Q̂[k−1] I − αA[k]

2
1
0
0

50

100
TOTAL DEGREE

150

200

3

Cnbroadcast :=

N
X
k=1

Qnk

and

Cnreceive :=N
X

BROADCAST

Overall, (Q)ij summarizes how well node i cancommunicate with node j using dynamic walks.
The corresponding row and column sums

2
1
0
0

Qkn

20

40

60
80
TOTAL DEGREE

100

120

140

k=1

introduced in [3] are therefore centrality measures that quantify how effectively node n canbroadcast and receive dynamic messages acrossthe network. These measures generalize the classic Katz centralities [4, 6] in the sense that they
become equivalent for a single time point.
We give here some new computations on avoice call dataset from MIT [1], comparing foreach node the total number of edges (total degree) against the broadcast centrality measure.
The data was sumarized into one-day windows,so (A[k] )ij = 1 means that person i talked to person j at least once on day tk . We collected dataover a period of 365 days and the results for two

Figure 1: Total degree (horizontal axis) versus
broadcast centrality (vertical axis) for voice calldata of participants within non-overlapping 30
day periods. A dynamic communicator is circled
in red.

2

A Model for Dynamic Communicators

To explain a possible mechanism for the emergence of dynamic communicators, a model has
recently been developed to simulate such behavior [5]. This model uses the general discrete-time

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

84tions (as measured by its total degree) and yet
has the greatest ability to communicate throughthe network (as measured by broadcast centrality).
In summary, we have illustrated a computational tool for discovering good communicators
in dynamic network data, and outlined an intuitively reasonable model that reproduces the ‘dynamic communicator’ effect seen in mobile communication data.
0.4
BROADCAST

discrete-space Markov chain framework of [2].
Here, given the current state of the network, aparticular model specifies the probability of eachedge existing at the next time point. This gives anatural and flexible way to incorporate whateverrules are felt to be appropriate.
To generate dynamic communicators, we willquantify a hierarchy amongst the nodes. Hierarchy, either explicitly imposed or implicitly selforganized, exists, for example, in businesses, educational establishments, military units, socialgroups and criminal networks. Our new model([5]) represents the hierarchy of communicatorsby allocating a fixed level of importance for eachnode. The model is based on the idea that nodes
near the top of the hierarchy have a greater levelof ‘influence’ or ‘prescience’ in the sense thatthose who receive their messages tend to passthat message on. A key ingredient of the modelis that nodes respond to messages they receive bysending out messages to other nodes. The levelof response is based partly upon the ratio of theimportance of the nodes who sent the messages
and the largest importance value in the network.
In this way, we have the possibility of producing ‘low-bandwidth’ nodes that do not appear
to be active when viewed over single snapshotsor across an aggregate summary, but have relatively high dynamic broadcast centrality becauseof their importance in the hierarchy. Such nodes
represent the dynamic communicators that we
illustrated above on the MIT data.
The new computation in Figure 2 shows theresults of an average of 40 independent simulations with the model for a network of 40 nodes
sending messages for 365 time points, using α =0.3. The node with the largest importance inthe dynamic network is circled in red. This red
circled node is a dynamic communicator—it has
taken part in a very modest number of interac-

0.3
0.2
0.1
0
19

20

21
22
TOTAL DEGREE

23

24

Figure 2: Total degree (horizontal axis) versus
broadcast centrality (vertical axis) for the average of 40 independent simulations of 40 nodes
over 365 time points.

References[1] Nathan Eagle, Alex S. Pentland, and David Lazer. Inferring friendship network structure by using mobile phonedata. Proc. Nat. Acad. Sci., 106(36):15274–15278, September 2009.
[2] Peter Grindrod and Desmond J. Higham. Evolving graphs:
Dynamical models, inverse problems and propagation.
Proc. Roy. Soc. A, 466:753–770, 2010.
[3] Peter Grindrod, Desmond J. Higham, Mark C. Parsons,and Ernesto Estrada. Communicability across evolving
networks. Physical Review E, 83:046120, 2011.
[4] L. Katz. A new index derived from sociometric data analysis. Psychometrika, 18:39–43, 1953.
[5] A. Mantzaris and D. J. Higham. A model for dynamic
communicators. submitted, 2011.
[6] M. E. J. Newman. Networks an Introduction. Oxford Univerity Press, Oxford, 2010.

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85

Examining the Social Decomposition of Mobile Call Graphs
Derek Doran*

Veena Mendiratta, Chitra Phadke, and Huseyin Uzunalioglu

Dept. of Computer Science & Engineering
University of Connecticut
Storrs, CT, 06269
Email: derek.doran@engr.uconn.edu

Bell Laboratories
Alcatel-Lucent
Murray Hill, NJ, 07974
Email: {veena.mendiratta, chitra.phadke, huseyin.uzunalioglu}@alcatel-lucent.com

Abstract—In this paper, we examine the social decomposition of amobile call graph using a new approach to measure the social tie strength
between users. This approach improves upon the existing measures byconsidering any number of observations about the calls made and the
local social structure. The results suggest that the dyadic hypothesis isvalid in the context of mobile call graphs, and that strong ties play a
critical role in the structure of the graph.

I. I NTRODUCTION
Social Network Analysis (SNA) is a powerful approach used tobetter understand the behaviors and relationships of users. SNA istraditionally applied in the context of online social networks (OSNs)such as Facebook, Flickr, and Twitter, where users can directly
establish ties, share information, and join groups to connect to users
with similar interests. In these networks, SNA operates over attributes
that directly imply a social connection between users. For example,
the fact that two users are friends on an OSN, that they belong to
the same groups, or that they share information with each other caneach be used individually to infer that a social tie exists.
Such OSNs contain causal information, that is, data attributes
which imply the existence of a social tie. There exist other social
networks, however, where only the effects of a social tie are observable. Each effect, taken alone, does not directly suggest tie strength.
Mobile call graphs are an example of such a social network. In amobile call graph, the effects of a strong social tie may include alarge number of calls placed, a long time spent talking, and manycalls during weekend and evening hours. By themselves, however,none of the attributes directly imply the tie strength. For example, auser may call a bank to check balances and pay bills more times thanthey call a friend, even though friendship is a stronger social tie.
Mobile call graphs represent the way in which a large number ofusers communicate with each other, and these patterns of communication are related to the social ties between people. Thus, studies
that apply SNA to mobile call graphs are rising in popularity [1], [2],[3], [4], [5]. Such studies, however, only pick a single feature about
the calls made between two users to define a social relationship. As
a result, the conclusions drawn by these studies are based only on asingle effect of a possible social relationship that exists. In order tomake observations about a call graph that more faithfully considers
the social relationship between users, an improved measure of tie
strength is needed.
In this paper, we propose an algorithm to quantify social tie
strength through the synthesis of many calling attributes whose values
are the effects of a social tie. We demonstrate that the resulting tie
strengths support the dyadic hypothesis, which states that tie strengths
depend primarily on the nature of the relationship between the twousers and is independent on local social network structure [6]. Localsocial structure is shown to still play a role, however, when the*Derek Doran performed this work as a summer intern at Bell Labs.

amount of interaction between two users is small. We then apply thealgorithm to a call graph provided by a major mobile service provider
to study the relationship between tie strength and call graph structure.
We decompose the mobile call graph by eliminating different levelsof the weakest or strongest social ties. Through the new definitionof social tie strength, we make observations that further supports the
dyadic hypothesis and emphasizes the importance of strong social
ties. Specifically, we find that:• Users connected by strong social ties have a positively correlated out-to-in degree distribution, while the out-to-in degree
distribution for nodes with weak ties are uncorrelated.
• There exist critical tie strength values where most of the connected components of the graph disintegrate.
• The massive connected component, which over 84% of allvertices are a member of, quickly fragments when strong social
ties are eliminated.
• The structure of the massive connected component is highlyrobust to the failure of edges with weak social ties.
The layout of this paper is as follows. In Section II, we introduce
our algorithm to derive social tie strength. Section III applies the algorithm to a mobile call graph and examines its social decomposition.
Section IV concludes the paper and outlines our future work.
II. T IE S TRENGTH A LGORITHM
We define a mobile call graph as a simple directed graph G =(V, E) where the set of vertices V represent mobile phone users,
and an edge e = (a, b) ∈ E iff a, b ∈ V and a placed a call to b. G
represents the |E| = m calls that are placed between the |V | = n
users. The objective is to define a weighting function S : E → R
that maps every directed edge to a value quantifying the strength of
the social tie between the users incident on the edge.
In our proposed algorithm, we represent each edge as a vectorof k attributes and compose them into the m × k matrix E, wherea row of E corresponds to an edge of G and [E]ij is the value of
attribute j for edge i. In order to map the row vectors of E to a value
representing social strength, we first apply an approach, inspired byprinciple component analysis (PCA) [7], that projects the data onto
a subspace which better represents the variation existing within thedata. This projection is motivated by recognizing that the true social
tie strength affects the value of the attributes that exhibit the greatest
variation the most. The projection uses an orthogonal basis set ofvectors that point in the directions where the variation in the datais the largest (referred to as principle components (PC)). This set isgiven by the eigenvectors of the covariance matrix Σ of E [7].
The sum of the eigenvalues of Σ is equal to the total variancewithin the data, which is the same as the dimensionality of the data if
it has zero mean and unit variance. This means that the eigenvalues ofΣ relate the amount of variation that is explained by each dimensionof the projected data to the variation along the dimensions of the

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86(a) Call length(b) Number of calls(c) Neighborhood overlap

Fig. 1: Correlation between social tie strength and selected attributes

original data. We thus take each component of the projected data,
multiply it by the corresponding eigenvalue, and then sum theseweighted components to get a value for social tie strength. This gives
the dimensions where the data exhibits very large variation additional
influence in the tie strength value. The entire algorithm is summarized
as follows.
Pm11) Set [E]ij = [E]ij − m
i=1 [E]ij for all j.
2) Set [E]ij = [E]ij /σj , with σj2 as the variance of attribute j.
3) Find the covariance matrix Σ of E.
4) Find Λ, a k × 1 column vector where Λi is the ith largest
eigenvalue of Σ.
5) Find U, a k × k matrix whose ith column is the righteigenvector corresponding to Λi .
6) The social tie strength for edge ei is given by ith componentof the vector S = EUΛ.
III. S OCIAL D ECOMPOSITIONIn this section, we apply the social tie strength algorithm to alarge mobile call graph and analyze the way its structure decomposes
as edges with weak social ties are removed. The mobile call graphcontains over 3M vertices and over 4.5M edges. The graph was
extracted from a dataset of approximately 10M calls.
A. Social tie strength
To apply the social tie strength algorithm we chose three attributes,
namely, the total length of all calls between two users, the total
number of calls placed between two users, and the neighborhood
overlap of two users (defined as the Jaccard similarity between theneighbor sets of each user). Figure 1 shows the relationship between
the derived social tie strength value and the attributes used. Figure 1aand 1b demonstrate that the social tie strength value increases with
the number of calls placed and the total amount of time users spend
talking to each other, which are attributes that reflect the direct
relationship between the users. We also see that the neighborhood
overlap plays a role for the large number of edges with a smallsocial tie strength. Since these edges place very few calls and havea small total call length, their social tie strengths are differentiatedbased on their neighborhood overlap. For edges that have a largesocial tie strength, the neighborhood overlap losses it’s influence.
This is because the neighborhood overlap contains a smaller amountof variation compared to the call length and total number of calls
attributes.
The positive correlation between tie strength and the attributes
reflecting the direct relationship between two users support applying
the dyadic hypothesis to the social ties of users in a mobile call graph.
Intuitively, the dyadic hypothesis should apply to call graphs because

the act of calling someone by phone is a deliberate, directed actionthat is generally not influenced by the structure of the social network
between the caller and callee. It is only when the calling interactions
between two users are weak that the local social network structure isgiven a significant amount of influence over the tie strength value.
Next, we analyze the structural characteristics of the mobile callgraph as the network is decomposed by removing edges with weaksocial ties.

B. Degree correlation
The in- and out-degree of a node in a mobile call graph reflects
the number of unique people that call a user and the number of
unique users that a person calls. It has been observed that the degree
distribution of many types of social networks, including mobile
call graphs, follow a power-law distribution [1], [8], [9], [10]. This
indicates that while the mean degree of a node may be small, thedistribution across the entire network exhibits a very large variance.
Equally important for telecom operators is understanding therelationship between the in-degree and out-degree of a node in a
mobile call graph. For example, it may the case that nodes with alarge in- but small out-degree represent call centers that receive alarge number of calls but do not place any. Nodes with large outbut small in-degrees may represent users that treat use their mobile
phone number as a business line, placing many phone calls that they
do not expect to get call backs from.
In Figure 2, we plot the mean out- or in-degree for all nodes witha given in- or out-degree as we remove edges from the call graph,keeping only the top percent of edges that have the highest social
tie strength. For example, the upper-left most plot considers the callgraph with just top 95% of the edges with highest social tie strength,
while the bottom-right plot includes only the top 5% of edges. In
every plot, we find that the nodes maintain a positive in-to-out degreecorrelation. Thus, despite the strength of their social relationships,
users that receive calls from many people also tend to make calls
to many people. When examining the out-to-in degree correlation,
however, we see that when the weak social ties are included there is
no relationship but as the weak social ties are removed the correlation
becomes positive. This suggests that when a user places a call alonga strong social tie it is likely to reciprocate a call back, but calls madeover weak social ties will not encourage the caller to be contacted
by other users. This matches our intuition about the influence of thesocial ties between people. If two users have a strong social tie, a callmade in one direction will likely lead to a call in the other directionin the future.

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8745% strongest ties remain that the number decreases. It is only after
the strongest 25% of ties are left that the number of connectedcomponents begin to increase. The knee in the trend at 25% likely
represents the point where the structure of the massive connectedcomponent finally shatters into multiple, smaller components. The
decrease in the number of connected components starting at 45%,
then, represents a critical tie strength where the other, non-massive
components begin to break down.
These observations suggest that the connected components of amobile call graph are structurally robust against the loss of edges withweak social ties. Furthermore, we find that the edges most criticalto the structure of the massive connected components are those withstrong social ties. This suggests that weak social ties are, generally,
not the critical connections that maintain the reachability of mostnodes in a mobile call graph.
Fig. 2: In- and out-degree distributions as weak ties are removed

IV. C ONCLUSIONS AND F UTURE W ORKThis paper presented a preliminary study on the social decomposition of a mobile call graph. The study uses a novel approach toquantify the social tie strength between two users that offers higher
fidelity by considering any number of observations. The social ties
suggest that the dyadic hypothesis is valid for mobile call graphs. By
examining the decomposition of the call graph, we find that strongsocial ties are critical to maintain the structure of the network. Future
work seeks to examine additional structural properties, including
shortest paths, clustering coefficients, and neighborhood distributions.
Additional attributes will also be incorporated for deriving thestrength of social ties.
R EFERENCES(a) Massive SCC size(b) Number of SCCs in graphFig. 3: Size and fragmentation of the massive connected component

C. Connected components
Next, we examine the relationship between strong social ties andthe connected components of a call graph. The distribution of thesizes of connected components are indicative of the reachability
between users, and is important when analyzing the diffusion of
information across a mobile call graph. It has been demonstrated thatmobile call graphs contain a single massive connected component [1],[2], which means that information introduced into the network has thepotential to reach most users. Whether or not strong or weak social
ties are critical to the structure of this massive connected component
is an important consideration when studying the spread of information
across a mobile call graph.
Figure 3 demonstrates the way in which the massive connectedcomponent breaks down as we gradually eliminate the weakest andstrongest social ties from the graph. In Figure 3a, we observe that the
number of nodes in the massive connected component decrease faster
when the edges are removed in the order of strongest to weakest tie
strength. In Figure 3b we observe that the total number of connectedcomponents in the entire network rise quickly when the strongest
ties are removed first. This sudden rise in the number of connectedcomponents, combined with the observed decrease in the size of themassive connected component as the strongest ties are removed first,
suggest that the loss of the strongest social ties breaks apart themassive component into multiple, smaller connected components.
When the weakest ties are removed first, the number of remaining
connected components exhibits a contrasting pattern. The numberof connected components remain constant, and it is not until the[1] K. Dasgupta, R. Singh, B. Viswanathan, D. Chakraborty, S. Mukherjea,
and A. Nanavati, “Social Ties and their Relevance to Churn in Mobile
Telecom Networks,” in Proc. of 11th ACM Intl. Conference on Extending
Database Technology, 2008.
[2] J.-P. Onnela, J. Saramaki, J. Hyvonen, G. Szabo, D. Lazer, K. Kaski,
J. Kertesz, and A.-L. Barabasi, “Structure and tie strengths in mobile
communication networks,” Proceedings of the National Academy of
Sciences of the United States, vol. 104, pp. 7332–7336, 2007.
[3] Y. Richter, E. Yom-Tov, and N. Slonim, “Predicting customer churn inmobile networks through analysis of social groups,” in Proc. of SIAM
Intl. Conference on Data Mining, 2010.
[4] M. Seshardi, S. Machiraju, A. Sridharan, J. Bolot, C. Faloutsos, andJ. Leskovec, “Mobile Call Graphs: Beyond Power-Law and Lognormal
Distributions,” in Proc. of 14th ACM Conference on Knowledge Discovery & Data Mining, 2008.
[5] A. Nanavati, S. Gurumurthy, G. Das, D. Chakraborty, K. Dasgupta,
S. Mukherjea, and A. Joshi, “On the Structural Properties of Massive
Telecom Call Graphs: Findings and Implications,” in Proc. of 15th ACM
Conference on Information and Knowledge Management, 2006.
[6] M. Granovetter, “The Strength of Weak Ties,” American Journal of
Sociology, vol. 78, pp. 1360–1380, 1973.
[7] J. E. Jackson, A User’s Guide to Principal Components. John Wiley& Sons, 2004.
[8] J. Kunegis, A. Lommatzsch, and C. Bauckhage, “The Slashdot Zoo:
Mining a Social Network with Negative Edges,” in Proc. of 18th ACM
Conference on the World Wide Web, 2009, pp. 741–750.
[9] A. Mislove, H. S. Koppula, K. Gummadi, P. Druschel, and B. Bhattacharjee, “Growth of the Flickr Social Network,” in Proc. of ACM SIGCOMM
Workshop on Online Social Networks, 2008.
[10] A. Mislove, M. Marcon, K. Gummadi, P. Druschel, and B. Bhattacharjee,“Measurement and Analysis of Online Social Networks,” in Proc. of the
ACM Internet Measurement Conference, 2007.

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88Time allocation in social networks: correlation between social
structure and human dynamics
Giovanna Miritello1,2 , Rubén Lara2 , and Esteban Moro1,3,4
1

Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, 28911 Leganés, Spain2
3

4

Telefónica Research, Madrid, Spain

Instituto de Ciencias Matemáticas CSIC-UAM-UCM-UC3M

Instituto de Ingenierı́a del Conocimiento, Universidad Autónoma de Madrid, 28049 Madrid, Spain

July 15, 2011
Abstract
Recent research has shown the deep impact of the dynamics of human interactions on the
spreading of information, opinion formation, etc. [1, 2, 3, 4, 5]. In general, the bursty natureof human interactions lowers the interaction between people to the extent that both the speed
or reach of information diffusion is diminished [2, 3]. Using a large database of 20 million usersof mobile phone calls we show evidence that there is a large correlation between this effect andthe social topological structure around a given interaction. In particular we show that social
relations of hubs in a network are relatively weaker from the dynamical point than those thatare poorer connected. This means that, dynamically, hubs have a relatively lower importanceon information transmission than poorer connected people. A detailed analysis shows that
this happens because hubs tend to allocate time in an efficient way so that they manage a(bounded) number of social interactions within a given time interval. We propose a model oftime allocation to explain the observed phenomena and discuss the importance of our resultsin general problems of information diffusion, coordination, opinion formation etc. in social
networks.

References[1] A. Vazquez et al. Impact of non-Poissonian activity patterns on spreading processes. Phys. Rev. Lett.
(2007) vol. 98 pp. vol. 98 (15) pp. 158702[2] G. Miritello, E. Moro, and R. Lara, Dynamical strength of social ties in information spreading. Phys.
Rev. E (2011) vol. 83 (4) pp. 045102[3] M. Karsai et al., Small but slow world: How network topology and burstiness slow down spreading.
Phys. Rev. E (2011) vol. 83 (2) pp. 025102[4] A. Gautreau, A. Barrat, and M. Barthélemy, Microdynamics in stationary complex networks. P. Natl
Acad Sci Usa (2009) vol. 106 (22) pp. 8847-52[5] C. Cattuto et al. Dynamics of person-to-person interactions from distributed RFID sensor networks.
PloS one (2010) vol. 5 (7) pp. e11596

1

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NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

90Correlated bursty behaviour in human communicationMárton Karsai,1 Kimmo Kaski,1 A.-L. Barabási,2, 3 and János Kertész3, 1
1
2
3

BECS, School of Science, Aalto University, Helsinki, Finland

Center for Complex Networks Research, Northeastern University, Boston, MA 02115

Institute of Physics, Budapest University of Technology and Economics, Budapest, Hungary

Temporal inhomogeneities occur frequently in human dynamics as a result of decision making
by individuals and of various kinds of correlations due to the social environment. These systems arecharacterized by intermittent switching between periods of low activity and those of high activity
bursts as observed, e.g., in digital records of human communication through different channels (seeFig.1) where competing tasks with different priorities have been proposed as partial explanationof the origin of burstiness and of the scale-invariance of the inter-event time distribution [1, 2]. It
has also been claimed that the broad inter-event time distribution can be explained by aggregatedrandom cascading behaviour of individuals where homogeneous Poisson cascades evolve on a shorttime scale with a time dependent rate induced by a long-term non-homogeneous random process
following circadian periodicities [3]. However, the circadian and weekly fluctuations explain only
partially the heterogeneous dynamics of human communication since by removing them the broad
distribution of inter-event times remains unchanged with a similar shape as the original event
sequence [4].

FIG. 1: Mobile-call activity of single individuals with color-coded inter-event times.

For systems with discrete events dynamics it is usual to characterize the observed temporal
inhomogeneities by the inter-event time distributions, P (tie ), where tie = ti+1 − ti denotes thetime between consecutive events. A broad P (tie ) reflects large variability in inter-event times. Yet
P (tie ) alone tells nothing about the presence of correlations, which is usually characterized by the
autocorrelation function, A(τ ), or equivalently by the power spectrum density. However, for temporally heterogeneous signals of independent events with power-law fat-tails in P (tie ) the Hurst
exponent and the autocorrelation function can indicate false positive correlations. To understand

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2
the mechanisms behind these phenomena, it is important to know whether there are true correlations in these systems. Therefore, for the systems showing fat-tailed inter-event time distributions,
there is a need to develop new measures that are sensitive to correlations but insensitive to fat
tails.
A sequence of events separated by inter-event times smaller than ∆t form a bursty period.
Let P (E) be the distribution of the number of bursty events E in such a bursty cascade. For∗

uncorrelated events we have P (E) ∼ e−E/E irrespective of the inter-event time distribution.
Our Analysis of mobile phone data shows that besides the fat tailed inter-event time distributions we also have a power-law distribution in P (E) ∼ E −β indicating intrinsic correlations inhuman communication activity (see Fig.2).

FIG. 2: Average P (E), P (tie ) and A(τ ) characteristic functions calculated form the mobile-call data.

These correlations can be attributed to memory effects. We define a memory function as theprobability of having the n + 1-st event in a bursty period, provided it has already contained nevents:∞P (E)p(n) = Pn=E+1∞
n=E P (E)P

which scales as:

1 − p(n) ∼ n−(β−1)(1)

This relationship has also been verified using mobile phone data.

[1] Barabási A.-L. The origin of bursts and heavy tails in human dynamics. Nature 435 207-211 (2005).
[2] Vázquez, A. et al. Modeling bursts and heavy tails in human dynamics Phys. Rev. E 73, 036127 (2006).
[3] Malmgren, R.D. et al. A Poissonian explanation for heavy tails in e-mail communication. Proc. Natl.
Acad. Sci. 105, 18153-18158 (2008).
[4] Jo, H-H., Karsai, M., Kertész, J, Kaski, K. Circadian pattern and burstiness in human communication
activity. arXiv :1101.0377 (2011).

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92Ethnic Segregation in the Area of Residence,
Work, and Free-time
Evidence from Mobile Communication
Ott Toomet∗†‡Siiri Silm‡

Erki Saluveer‡

Rein Ahas‡

July 15, 2011

Abstract
This paper analyzes spatial ethnic segregation using cellphone data.
The data allows us to differentiate between place of residence, work, andfreetime; and between ethnic majority and minority groups. We focus
on individual pairwise meeting potential (copresence) between ethnic majority and minority groups in an European city (Tallinn, Estonia). We
show that the potential number of meetings with the other populationgroup at workplace is only weakly related to the ethnic composition of
the neighborhood of residence, and that in freetime is almost completely
unrelated. These results suggest that physical separation of ethnic groups
in segregated neighborhoods is of a less concern than suggested by placeof residence data only.

∗ Corresponding author. e-mail: otoomet@gmail.com
2 Department of Economics, Tartu University, Narva 4, Tartu 51009, Estonia
3 Department of Human Geography, Tartu University

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1

Introduction

Everyday observations suggest that immigrants are fairly separated from thenative population. A number of studies indicate that this is (partially) causing
their inferior labor market and educational outcomes (Clark and Drinkwater,
2002; Card and Rothstein, 2007). Mainly due to data availability, the bulk of
the literature focuses on the place of residence and related segregation. Morerecently, availability of matched employer-employee data has allowed to perform similar analyzes on workplaces (see, for instance Åslund and Skans, 2005;
Hellerstein and Neumark, 2008).
However, although the existence of substantial segregation both at home andwork is well established, the importance of these facts is unclear. Anecdotal evidence suggest that our most important contacts (besides of the close family) areneither neighbors nor colleagues. In that case, the above mentioned segregation
might be of a less concern.
Unfortunately, there is very little evidence about segregation in dimensions
other than place of residence and work. In particular, there is very little data
available about the social life in free-time. The existing studies, based onfriendship ties of school-age children suggest a substantial segregation as well,where members of many minority groups tend to socialize with friends of similar
type (Currarini, Jackson, and Pin, 2009; Martinovic, van Tubergen, and Maas,
2009; Currarini, Jackson, and Pin, 2010). Survey-based evidence also indicatesthat leisure time contacts with the majority population does not depend on theneighborhood ethnic composition (Martinovic, van Tubergen, and Maas, 2009).
The current paper complements this literature by analyzing the segregation
based on cellphone usage data. We observe the location (sender) and time ofevery call in a cellular network in a bilingual city (Tallinn, Estonia) of about
500 000 inhabitants. In addition, we also observe the preferred language (majority or minority) of all the callers. This allows us to analyze segregation (based on
proximity in space and time) in the area of residence, work, and outside of these
two regions. The previous research indicates that time-spacial copresence is agood indicator for social ties (Crandall, Backstrom, Cosley, Suri, Huttenlocher, and Kleinberg,2010) in certain circumstances.
Our analysis indicate that the workplace segregation is only moderately associated with the ethnic composition of place of residence, and segregation in
freetime activities is virtually unrelated to it. The results are robust with respect to choice of spatial units. Hence the results suggest that despite of thesubstantial residential segregation, both minority and majority group members
have the potential to meet each other at work or in free time.
The paper continues as follows: the next sections discusses the concept ofcopresence and homophily, and explains how these are computed. Section 3
is devoted to the data description, Section 4 presents the results, the following
section provides several robustness results, Section 6 discusses the main findings,
and the last section concludes.

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94References
Card, D., and J. Rothstein (2007): “Racial segregation and the black-white
test score gap,” Journal of Public Economics, 91(11-12), 2158 – 2184.
Clark, K., and S. Drinkwater (2002): “Enclaves, neighbourhood effects andemployment outcomes: Ethnic minorities in England and Wales,” Journal of
Population Economics, 15, 5–29, 10.1007/PL00003839.
Crandall, D. J., L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher,and J. Kleinberg (2010): “Inferring social ties from geographic coincidences,” Proceedings of the National Academy of Sciences of the US, 107(52),22436–22441.
Currarini, S., M. O. Jackson, and P. Pin (2009): “An Economic Model of
Friendship: Homophily, Minorities, and Segregation,” Econometrica, 77(4),1003–1045.
(2010): “Identifying the roles of race-based choice and chance in highschool friendship network formation,” Proceedings of the National Academyof Sciences of the US, forthcoming.
Hellerstein, J. K., and D. Neumark (2008): “Workplace Segregation in
the United States: Race, Ethnicity, and Skill,” Review of Economics andStatistics, 90(3), 459–477.
Martinovic, B., F. van Tubergen, and I. Maas (2009): “Dynamics ofInterethnic Contact: A Panel Study of Immigrants in the Netherlands,” European Sociological Review, 25(3), 303–318.
Åslund, O., and O. N. Skans (2005): “Will I see you at work? Ethnic
workplace segregation in Sweden 1985-2002,” Working Paper 2005:24, IFAU,
P.O. Box 513, 751 20 Uppsala, Sweden.

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95

Risk and Reciprocity Over the Mobile Phone Network:
Evidence from Rwanda
Joshua Blumenstock

Nathan Eagle

Marcel Fafchamps

University of California, Berkeley

Santa Fe Institute

Oxford University

DRAFT VERSION
June 2011∗

Abstract:
A large literature describes how local risk sharing networks help individuals deal with idiosyncratic
economic shocks. The majority of the empirical evidence focuses on the beneﬁts of risk sharing within small,
local communities. When entire communities face the same shock, and when the transaction costs of transfers
are high, these risk sharing networks are likely to be less eﬀective. In this paper, we document how a newtechnology – mobile phones – reduces transaction costs and enables Rwandans to share risk quickly over long
distances. Examining usage logs obtained from the monopoly telecommunications provider, and exploiting
an earthquake that devastated the Lake Kivu region of Rwanda, we show that a large and anomalous volumeof an early form of “mobile money” was sent to people aﬀected by the earthquake in the days immediately
following the quake. Accounting for increases in adoption over time, we estimate that between $22,000
and $30,000 would be transferred in response to a current-day earthquake. Though the eﬀect is large, thebeneﬁts are heterogeneous: after controlling for a variety of factors, it is the wealthy individuals, and thoseindividuals with contacts living in a disperse geographical region, who are most likely to receive a transfer
after the earthquake. We further show that the patterns of transfers sent in response to the earthquake aremost consistent with a model of risk sharing, rather than alternative models of remittances or pure altruism.

Keywords: Risk Sharing; Mobile Phones; Information and communications technologies; Development;
Earthquakes; Rwanda; Africa.

∗ email: jblumenstock@berkeley.edu. Address: 102 South Hall, Berkeley, CA 94720, USA. Telephone: 1 (510) 642-1113.
The authors are grateful for thoughtful comments from Alain de Janvry, Frederico Finan, Ethan Ligon, Jeremy Magruder,
Edward Miguel, and to seminar participants at the Berkeley Development Lunch for helpful comments. We gratefully acknowledge ﬁnancial support from the International Growth Center, the National Science Foundation, and the Institute for Money,
Technology and Financial Inclusion. All errors are our own.

1
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1

Introduction

In developing countries, where social safety nets are rare and formal markets for credit and insurance arethin, people frequently rely on friends and family for support in times of trouble. A large literature describes
how such risk sharing networks can allow individuals to eﬀectively smooth consumption over time and in theface of short-term economic shocks (Fafchamps & Gubert 2007, Townsend 1994, Udry 1994, Deaton 1992).
However, these networks work best at insuring against idiosyncratic shocks that are uncorrelated acrossmembers of the same network. When a large covariate shock aﬀects an entire community, local risk sharing
networks are less eﬀective.1 While individuals could in principle receive support from friends and familyliving outside of the aﬀected region, sending money over distance is costly and individuals are quite sensitive
to the cost of remitting (Yang 2008). More critically, aﬀordable mechanisms for transferring money overlong distances often do not exist. In much of East Africa, for instance, formal money transfer systems suchas Western Union are only available in major urban areas, and informal methods (such as sending moneywith a public bus driver) are slow, intermittent, and expensive. Thus, the empirical evidence indicates thatin-kind and monetary transfers typically occur between friends and family within small, local communities.2
In an increasing number of developing countries, the mobile phone network has begun to provide a newmechanism for interpersonal transfers which could potentially remove the geographic constraint from risk
sharing relationships. “Branchless banking” systems, with over 80 deployments worldwide, allow individuals
to transfer “mobile money” from one phone to another at a fraction of the cost of existing alternatives(McKay & Pickens 2010). Typically, a mobile subscriber types in the phone number of the recipient andthe amount to be transferred, and the balance is deducted from the sender’s account and added to therecipient’s. The transaction takes a few seconds to complete, and costs at least 50 percent less than what
it would cost to send money through traditional channels (Ivatury & Mas 2008). Beyond the convenienceand reduction in transaction costs, mobile banking systems are noteworthy for their increasing ubiquity. For
instance, a recent study in Kenya found that although only 23 percent of adults owned a bank account, over
50 percent of adults were registered users the mobile banking system (FSD Kenya 2009).3 Worldwide, it isestimated that by 2012 there will be 1.7 billion people with a mobile phone but no bank account (CGAPand GSMA 2009).
1 See, for instance, evidence on limited giving in response to famines in India (Sen 1983, Dreze & Sen 1991).
2 Udry (1994), for instance, observes that 75 percent of surveyed Nigerian households made informal loans, but that almost allloans occurred within a village. Fafchamps & Gubert (2007) similarly observe that geographic proximity is a major determinantof sharing patterns: when two households live near each other, it is more likely that the one will help the other. Kurosaki &Fafchamps (2002) and de Weerdt & Fafchamps (2010) obtain similar ﬁndings for Pakistan and Tanzania, respectively.
3 Over $200 million dollars is transferred over the Kenayan mobile phone network each day. Pulver (2009) estimates that
47% of the Kenyan population uses mobile phones as the primary method of sending money. Similarly, in surveys conductedby the ﬁrst author in Rwanda in July 2009, we found that 97.3% of Rwandan phone users had heard of the Rwandan mobiletransfer service, and that nearly 80% had used it within the last year.

2
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In this paper, we explore one mechanism by which such mobile money systems may have a meaningful
economic impact on the lives of their users. We measure the extent to which individuals transfers funds
over the network in order to help friends and family cope with severe economic shocks. Speciﬁcally, we
test whether a large earthquake in Rwanda caused people in unaﬀected parts of the country to transfer
a rudimentary form of “mobile money” to people living close to the earthquake’s epicenter.4 Observing
the entire universe of mobile-based transfers occurring before and after the earthquake, we ﬁnd that theearthquake caused a large and signiﬁcant inﬂux of transfers to the people close to the epicenter. The eﬀect
is highly signiﬁcant on the day of the earthquake and the following day, but not on a number of “placebo”
days. We ﬁnd similar, albeit muted, eﬀects following a number of ﬂoods. Our results are robust to diﬀerent
estimation strategies. Though the total volume of money sent following the earthquake was small in absoluteterms – primarily because the banking service was launched shortly before the earthquake occurred – simple
calculations indicate that if a similar earthquake were to occur today, the current value of mobile money
sent would be roughly USD$22,000 to $30,000. This is particularly striking given the fact that, at the timeof the earthquake, the liquidity of airtime transfers was rather limited. As the capabilities of such mobilebanking systems expand and phone-based transactions become the norm, we would expect the volume (andutility) of such transfers to increase.
From a public policy perspective, we are also interested in identifying which types of people are most
likely to receive a mobile-based transfers, both in general and in response to exogenous shocks such as
the earthquake. To measure this heterogeneity, we exploit a rich source of data obtained from Rwanda’s
dominant mobile phone operator. Using the transaction records of all mobile phone activity occurring in
Rwanda from 2005 until 2009, we measure the mobile usage patterns and a number of properties of thesocial networks of each of Rwanda’s 1.5 million mobile subscribers. Combining this data with survey datacollected by the Rwandan government and phone surveys we conducted, we are further able to computea noisy measure of each individual’s economic status. We ﬁnd that wealthier phone users are signiﬁcantly
more likely to receive a transfer after the earthquake. And while individuals with a large number of contacts
are more likely to receive transfers on normal days, they are not signiﬁcantly more likely to receive a transfer
in the day of the earthquake. As discussed later in the paper, this ﬁnding implies that the distributionaleﬀects of phones – and mobile banking in particular – may be regressive.
Finally, we analyze patterns of giving after the earthquake to test between two stylized models of why
4 During the period we analyze, mobile subscribers were able to transfer balance from one phone to another using a service
called “Me2u.” This airtime could be used to make calls, could be resent to other subscribers, or could be sold informally for asmall commission. However, there were no formal outlets at which the airtime could be converted to cash, and at the time of theearthquake it could not be used to purchase goods. In February 2010, the telecommunications operator launched a fully-ﬂedged
Mobile Money service, similar to the M-PESA system in Kenya, which allows subscribers to convert airtime to cash, and which
will soon allow for over-the-counter purchases with airtime, as well as interest-bearing airtime accounts.

3
NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

98people are transferring funds over the mobile network. First, the transfers could be motivated primarily by
charitable feelings, by which we refer to a variety of motives for assisting others that do not rely on quid-proquo.5 Included in this type of model are remittance-like relationships where one person consistently depends
on the other for support. If this general altrusim/remittance behavior were dominant, there are several
implications: we would expect to see transfers increasing in the wealth of the sender relative to that of therecipient, and increasing with the distance between sender and recipient (because the sender is less likely tobe aﬀected by the same shock). We would also expect to observe transfers ﬂow from urban areas to rural
areas, and to occur more often in pairs of people where one person consistently sends and the other receives.
On the other hand, if transfers are largely motivated by expectations of reciprocity, as emphasized in therisk sharing literature, the empirical predictions are quite diﬀerent. In such arrangements, we would expect
transfers to decrease with the distance between sender and recipient, as the increase in distance may both
impinge a person’s ability to assess another’s true need (Vreyer et al. 2010), and may increase the diﬃcultyof monitoring and enforcing informal, reciprocal commitments (Ligon et al. 2002, Ligon 1998). We would
further expect to ﬁnd evidence that earthquake-related transfers occur more often in reciprocal relationships
where, for instance, at some point prior to the earthquake funds had been sent in the opposite direction (i.e.
from receiver to sender). By examining individual-level heterogeneity, and by comparing edges (dyads) of
the directed call graph, we test these predictions on the data from Rwanda. We ﬁnd that while the data arebroadly consistent with the latter model of risk sharing, there is little evidence that people give for purely
charitable motives in response to the earthquake.
The remainder of the paper is organized as follows. In Section 2 we outline our estimation strategy
to measure the eﬀects of the earthquake and potential heterogeneity. In Section ??, to help motivate theempirical analysis, we develop a simple model of phone-based interpersonal transfers that distinguishes
between “charitable” transfers and transfers with a quid-pro-quo expectation. The data are described in
Section 3, together with a basic background of mobile phone services in Rwanda. The empirical results are
discussed in Section 4, and a series of robustness checks are presented in Section 5. Section 6 concludes.
5 These include pure altruism (Becker 1976), inequality aversion (Fehr & Schmidt 1999), warm glow (Andreoni 1990), andsubjective reputational rewards (Bénabou & Tirole 2006).

4
NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

99Composite Social Network for Predicting Mobile Apps Installation
Wei Pan and Nadav Aharony and Alex (Sandy) Pentland
MIT Media Laboratory
20 Ames Street
Cambridge, Massachusetts 02139{panwei,nadav,pentland}@media.mit.edu

Introduction
We are interested in studying the network-based prediction for mobile applications (referred as “apps”) installation,
as the mobile application business is growing rapidly (Ellison 2010). This work also benefits the general theme ofsocial network-based personalization and recommendation.
However it was very difficult to adopt existing tools fromlarge-scale social network research to model and predict the
installation of certain mobile apps for each user due to thefollowing facts:
1. The underlying network is not observable. While many
projects assume phone call logs are true social/friendshipnetworks, others may use whatever network that is available as the underlying social network. On the other hand,smart phones can easily sense multiple networks using
built-in sensors and software: Call logs, bluetooth proximity, GPS co-location, surveys, etc. In this work, our key
idea is to infer an optimal composite network, the networkthat best describes diffusion, from multiple layers of different networks easily observed by modern smart phones.
2. Analysis for epidemics and Twitter networks (Yang andLeskovec 2010) is based on the fact that network is the
only mechanism for adoption; i.e. the only way to get theflu is to catch the flu from someone else. For mobile app,
this is, however, not true at all. Any user can buy an app
online directly without peer influence.
3. The individual behavioral variance in app installation
is so significant that any network effect might possibly
be rendered unobservable from the data. We have both“grandma” type users and geek users in the network.
4. There are exogenous factors in the app installation behaviors. One particular factor is the popularity of apps. For instance, the Pandora Radio app is vastly popular and highly
ranked in the app store, while most other apps are not.
Statistical analysis used by social scientists such as
matched sample estimation (Aral, Muchnik, and Sundararajan 2009) are only for identifying network effects and mechanism. Recently works in computer science are only applicable to artificial simulation data on real networks (Gomez RoThis work is originally published in Proceeding of AAAI 2011.
You can download the full version at http://arxiv.org/
abs/1106.0359

driguez, Leskovec, and Krause 2010) (Myers and Leskovec
2010). However, by making inference from data rather thanmaking assumptions, our model successfully addresses theabove issues in practical social-based prediction. To ourknowledge, we don’t see other related works for similar
problems.

Data
We collected our data from March to July 2010 with 55 participants, who are residents living in a married graduate student residency of a major US university. Each participant
is given an Android-based cell phone with a built-in sensing software developed by us. The software runs in a passive manner, and it didn’t interfere the normal usage of thephone. Please refer to Aharony et al.(Aharony et al. 2011)
for more information about data collections.
Our software is able to capture most data from phones.
We summarize all the networks obtained from both phones
and surveys in Table 1. Notice that we collected the affiliation network and the friendship network by deploying asurvey, which lists all the participants and ask each one tolist their affiliations (i.e. the academic department), and rate
their relationships. However we believe for app market makers the affiliation network can also be inferred simply byusing phone GPS/cell tower information as shown by Farrahi et al(Farrahi and Gatica-Perez 2010). We refer to all
networks in Table 1 as candidate networks, and all candidate networks will be used to compute the optimal composite network. It should be noted that all networks are nondirectional in this work. Our built-in sensing platform is constantly monitoring the installation of mobile apps. Overall,
we receive a total of 821 apps installed by all 55 users.

Prediction by Learning
In this section, we describe our novel learning model for
capturing the app installation behaviors in networks. In thefollowing content, G denotes the adjacency matrix for graph
G. Each user is denoted by u ∈ {1, ..., U }. Each app is denoted by a ∈ {1, ..., A}. We define the binary random variable xau to represent the status of adoption (i.e. app installation): xau = 1 if a is adopted by user n, 0 if not.
As introduced in the previous section, the different social
relationship networks that can be inferred by phones are de-

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

100Network
Call Log Network
Bluetooth Proximity Network
Friendship Network
Affiliation Network

Type
Undirected,Weighted
Undirected,Weighted
Undirected,Binary
Undirected,Binary

Source# of Calls# of Bluetooth Scan Hits
Survey Results (1: friend; 0: not friend)
Survey Results (1: same; 0: different)

Notation
Gc
Gb
Gf
Ga

Table 1: Network data used in this study.
noted by G1 , ..., GM . Our model aims at inferring an optimal composite network Gopt with the most predictive powerfrom all the candidate social networks. The weight of edgemei,j in graph Gm is denoted by wi,j
. The weight of an edge
opt
in G is simply denoted by wi,j .
Adoption Mechanism: One base idea of our model is the
non-negative accumulative assumption, which distinguishes
our model from other linear mixture models. We define Gopt
to be:X
Gopt =
αm Gm , where ∀m, αm ≥ 0.
(1)
m

The intuition behind this non-negative accumulative assumption is as follows: if two nodes are connected by a certain type of network, their app installation behaviors may or
may not correlate with each other; On the other hand, if two
nodes are not connected by a certain type of network, theabsence of the link between them should lead to neither positive or negative effect on the correlation between their app
installations. As shown in Table 2 in the experiment session,
our non-negative assumption brings significant performanceincrease in prediction. Non-negative assumption also makes
the model stochastic and theoretically sound. We treat binary graphs as weighted graphs as well. We later refer tothe vector (α1 , ..., αM ) as the optimal composite vector. We
continue to define the network potential pa (i):Xpa (i) =wi,j xaj ,(2)

where ∀u, su ≥ 0. su captures the individual susceptibility of apps, regardless of which app. We use the exponential function for two reasons: a) The monotonic and concave
properties of f (x) = 1 − exp(−x) matches with recent research (Centola 2010), which suggests that the probabilityof adoption increases at a decreasing rate with increasing
external network signals. b) It forms a concave optimization
problem during maximum likelihood estimation in modeltraining. As shown in the experiment section and based on
our experiences, this exponential model yields the best performance.
Model Training: We move on to discuss model training.
During the training phase, we want to estimate the optimalvalues for the α1 , ..., αM and s1 , ..., sU . We formalize it asan optimization problem by maximizing the sum of all conditional likelihood.
Given all candidate networks, a training set composed ofa subset of apps TRAIN ⊂ {1, ..., A}, and {xau : ∀a ∈TRAIN, u ∈ {1, ..., U }}, we compute:arg

f (s1 , ..., sU , α1 , ..., αM )
 Y
Y
= log
Prob(xau = 1|xau0 : u0 ∈ N (u))
a∈TRAIN u:xa
u =1

Y







(3)

The potential pa (i) can also be decomposed into potentials
from different networks:
X
X
m a
(4)
pa (i) =
αm (
wi,j
xj ),
j∈N (i){z

1 − Prob(xau = 1|xau0 : u0 ∈ N (u))

u:xa
u =0

m
N (i) = {j|∃m s.t. wi,j
≥ 0}.

pm
a (i)

(6)

where:

where the neighbor of node i is defined by:|f (s1 , ..., sU , α1 , ..., αM ),Subject to: ∀u, su ≥ 0, ∀m, αm ≥ 0j∈N (i)mmax

s1 ,...,sU ,α1 ,...,αM

=

X

X

a∈TRAIN

log(1 − exp(−su − pa (u))

u:xa
u =1


−

X

(su + pa (u))

(7)

u:xa
u =0

}

where pma (i) is the potential computed from one single candidate network. We can think of pa (i) as the potential ofi installing app a based on the observations of its neighborson the composite network. The definition here is also similar
to incoming influence from adopted peers for many cascade
models (Kempe, Kleinberg, and Tardos 2003).
Finally our conditional probability is defined as:
Prob(xau = 1|xau0 : u0 ∈ N (u)) = 1 − exp(−su − pa (u)),(5)(8)
This is a concave optimization problem. Therefore, global
optimal is guaranteed, and there exist efficient algorithms
scalable to larger datasets (Boyd and Vandenberghe 2004).
We emphasize that our algorithm doesn’t distinguish thecausality problem (Aral, Muchnik, and Sundararajan 2009)in network effects: i.e.,we don’t attempt to understand thedifferent reasons why network neighbors have similar app
installation behaviors. It can either be diffusion (i.e. myneighbor tells me), or homophily (i.e. network neighbors
share same interests and personality).

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

101Experiments1
Model Validation: Our algorithm predicts the probability of
adoption (i.e. installing an app) given its neighbor’s adoption
status. pi ∈ [0, 1] denotes the predicted probability of installation, while xi ∈ {0, 1} denotes the actual outcome. The
most common prediction
q P measures is the Root Mean Squaren12
Error (RMSE =
i=1 (pi − xi ) ), Mean Precision atnk (MP-k) and Optimal F1 -score (the largest F1 Score on thePrecision-Recall curve).
We test different configurations in Table 2 to check how
three major factors in our approach (networks, individual
variances and exogenous factors) affect prediction accuracy
by testing our algorithm with some factors omitted. We also
test our non-negative accumulation assumption here.
Conditions
Net.+ Ind. Var. + Exogenous Factor
Net. + Ind. Var. Only
Ind. Variance Only
Net. Only (non-negative)
Net. Only (allow negative)

RMSE
0.25
0.26
0.29
0.26
0.30

MP-5
0.31
0.29
0.097
0.24
0.12

F1
0.43
0.42
0.24
0.37
0.12

Table 2: The performance of our approach under five different configurations. We observe that modeling both individual variance and exogenous factors are crucial in performance as well as enforcing non-negative composition forcandidate networks as in Eq. 1.
In addition, we illustrate the prediction performance when
our algorithm is only allowed to use one single network. The
1
Due to space limitation, please refer to our full paper for detailon experiments: http://arxiv.org/abs/1106.0359.

results are shown in Fig. 1. We discover that our composite
network performs better than single network alone.
0.5
0.4
F1 Score

0.3
0.2

ch
Ap

pr

oa

Gc

Gb

Gf

an

do
m

0

Ga

0.1

O
ur

R

Virtual Network for Exogenous Factors: Obvious exogenous factors include the popularity and quality of an app,which affect the ranking and review of the app in the AppStore/AppMarket. We can model this by introducing a virtual graph Gp , which can be easily plugged into our composite network framework. Gp is constructed by adding avirtual node U + 1 and one edge eU +1,u for each actual user
u. The corresponding weight of each edge wU +1,u for computing pa (u) is C a , where C a is a positive number describing the popularity of an app. In our experiment, we use thenumber of installations of the app in this experimental community as C a . In practice for app market makers, we argue
that C a can be easily obtained accurately by counting app
downloads and app ranks.
The exogenous factors also increase accuracy in measuring network effects for a non-trivial reason: Consideringa network of two nodes connected by one edge, and both
nodes installed an app. If this app is very popular, then thefact that both nodes have this app may not imply a strong
network effect. On the contrary, if this app is very uncommon, the fact that both nodes have this app implies a strong
network effect. Therefore, introducing exogenous factors
does help our algorithm better calibrate network weights.

Figure 1: We demonstrate the prediction performances using
each single network here. For comparison, we also show theresult of random guess, and the result using our approach,which combines all potential evidence.
Comparison and Performance: We discover that ourmodel performs much better than alternative approaches,and predicts almost half of all apps users install (∼ 45%
precision at 45% recall). We demonstrate that our algorithm
performs very well in both predicting future app installation and in predicting with significant missing data. We referreaders to our full paper for details.

References
Aharony, N.; Pan, W.; Ip, C.; Khayal, I.; and Pentland, A. 2011.
The social fmri: Measuring, understanding and designing social
mechanisms in the real world. In Proceedings of the 13th ACM
international conference on Ubiquitous computing. ACM.
Aral, S.; Muchnik, L.; and Sundararajan, A. 2009. Distinguishinginfluence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences
106(51):21544.
Boyd, S., and Vandenberghe, L. 2004. Convex optimization. Cambridge Univ Pr.
Centola, D. 2010. The Spread of Behavior in an Online Social
Network Experiment. science 329(5996):1194.
Ellison, S. 2010. Worldwide and U.S. Mobile Applications, Storefronts, and Developer 20102014 Forecast and Year-End 2010 Vendor Shares: The ”Appification” of Everything.
Farrahi, K., and Gatica-Perez, D. 2010. Probabilistic Mining ofSocio-Geographic Routines From Mobile Phone Data. Selected
Topics in Signal Processing, IEEE Journal of 4(4):746–755.
Gomez Rodriguez, M.; Leskovec, J.; and Krause, A. 2010. Inferring networks of diffusion and influence. In Proceedings of the
16th ACM SIGKDD international conference on Knowledge discovery and data mining, 1019–1028. ACM.
Kempe, D.; Kleinberg, J.; and Tardos, É. 2003. Maximizing the
spread of influence through a social network. In Proceedings ofthe ninth ACM SIGKDD international conference on Knowledge
discovery and data mining, 137–146. ACM.
Myers, S., and Leskovec, J. 2010. On the convexity of latent social
network inference. Arxiv preprint arXiv:1010.5504.
Yang, J., and Leskovec, J. 2010. Modeling Information Diffusion
in Implicit Networks.

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102

Mobile phone and motorway traffic: a panel data perspective
Emmanouil Tranos, e.tranos@vu.nl
John Steenbruggen, john.steenbruggen@rws.nl
Peter Nijkamp, p.nijkamp@vu.nl
Henk Scholten, hscholten@feweb.vu.nl

Dept. of Spatial Economics, VU University
De Boelelaan 1105, 1081 HV Amsterdam
The Netherlands

The objective of our paper is to use mobile phone data as the main predictor of motorway traffic.
Such a modelling exercise can provide a useful tool for transport engineers as it will enable the(near) real-time estimation of car traffic in specific segments of motorways avoiding the use of othermore expensive and less efficient surveying techniques. Apart from mere academic interest in
utilizing this new and rich data source in a social science domain, potential applications could include
transport and incident management (Steenbruggen et al. 2011).
The case study for our research is the city of Amsterdam. The data we will utilize for this paper has
been supplied by a major telecom operator in the Netherlands and provides aggregated information
about mobile phone usage at the level of the GSM cell for the period 2007-2010. The spatial
dimension is comprised by c. 1200 cells. The temporal dimension provides information on an hourly
basis creating a very detailed pool of data. Indeed, such a rich dataset appears to be a ‘luxury’ forspatial analysts, but at the same time increases the complexity of the necessary analytical methods.
The mobile phone dataset will be used to create a key variable indicating the mobile phone usage
intensity. This variable will be the main explanatory variable of interest in the model we will build in
our research. As our objective here is to predict car density in specific motorway segments, thedependent variable for this model will be based on motorway traffic measures based on a detection
loop method. Such data has been made available to the research group and will be used here as thedependent variable. The main challenge is to spatially link these two heterogeneous datasets which
are different. In order to do so, an area selection process will take place. Although we have data
available for the city of Amsterdam, we will focus only on these cells that intersect with motorways
for which there is traffic data available from the detection loop dataset.
Nonetheless, mobile phone data is not the only predictor for car density. Consequently, a set ofcontrol variables will be built. These variables will include both temporal and spatial information. In
more detail, control – dummy – variables will be included in the model indicating times of interest
during the day such as rush hours, working hours and night time hours, as well as days of interest

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

103such as weekdays or weekends and bank holidays. Other temporal control variables of interest could
be the time of year (winter/summer) and holiday periods. In addition, land use variables will also be
incorporated in this model. Examples include the location of transport hubs or business areas inside
the cell, as these types of land use will increase the intensity of mobile phone usage in a cell. On thecontrary, land cover such as water-covered areas will be negatively related with phone usage.
From an econometric point of view, panel data will be the preferred specification for this model, as
it will enable us to take advantage of the two very detailed dimensions of the data (space and time).
Panel data improves the researchers ability to control for missing or unobserved variables (Hsiao
2003). Such an omitted-variable bias as a result of unobserved heterogeneity is a common
problem in cross-section models. In addition, potential selection bias can also be addressedmore efficiently with panel data. In total, panel data specification reduces the risk of obtaining
biased estimators (Baltagi 2001).
Altogether, we ‘will likely have around 100 cells of interest for a four year period (4years x 365days x
24hours) and without considering any potential data gaps we will achieve a panel data of c. 3.5
million observations. This will provide the basis for a robust modelling exercise. However, two
econometric challenges might occur here: temporal autocorrelation due to the repeated over timemeasurements, and also spatial autocorrelation because of the potential dependence of mobile
phone intensity between adjacent cells (Sevtsuk and Ratti 2010). In order to reveal these issues,
relevant econometric tests will be performed and alternative to OLS specifications will also be
utilized. In addition, the analysis of residuals will reveal information about cells and points in time
where and when our prediction was not successful.
In a nutshell, we are aiming to build a generic model in order to see if and how mobile phone use
intensity can be utilised as a predictor for car density and motorway traffic. Such a model will
provide the basis for a real-time system for traffic and incident management.

Reference list
Baltagi BH (2001) Econometric analysis of panel data. 2nd edn. John Wilet & Sons, Chichester
Hsiao C (2003) Analysis of panel data. 2nd edn. Cambridge University Press, Cambridge
Sevtsuk A, Ratti C (2010) Does Urban Mobility Have a Daily Routine? Learning from the Aggregate
Data of Mobile Networks. Journal of Urban Technology 17 (1):41-60
Steenbruggen J, Borzacchiello MT, Nijkamp P, Scholten H (2011) Mobile phone data from GSM
networks for trafﬁc parameter and urban spatial pattern assessment: a review ofapplications and opportunities. GeoJournal forthcoming

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

104Churn Analysis in Mobile Telecom Data using Hybrid Paradigms
Yeshwanth V and Saravanan M
Ericsson R & D
Ericsson India Global Service Private Ltd, Chennai, India{yeshwanth.v, m.saravanan}@ericsson.com
Abstract
Churn in the telecom domain refers to the
movement of customers from one operator network
to another. With the high number of operatorscompeting in every market ranging from thedeveloping to developed countries, the relevance tochurn has increased dramatically. The key point
being that the cost of bringing in a new customer ismuch greater than retaining and maintaining thevalue of an existing customer. While many churnprediction models are out in the market, whenapplied to the live environment many fail to live up
to their advertised capabilities. Being a
predominantly social phenomenon, it’s a challenge
to come up with a single good model. While
prediction models have grown leaps and bounds,
their application to the domain still remains thebottleneck. Our models and analysis have taken
into the account the vital cogs in the mobile
operator’s network and also by a proper data
preprocessing with respect to our needs. We have
employed a split data preprocessing techniquecoupled with a hybrid methodology to perform
churner prediction on a mobile network. The
hybrid approach has been tried out with different
classifier pairs to see the difference on two
different mobile telecom datasets. We havecompared the different pairs of classifiers withmore preference given to the hit rates of churners
while keeping the misclassification rate of nonchurners under an appreciable limit. We havefocused on improving the classification/prediction
model in this paper as an extension to an existingmodel [1] which has shown good results on aparticular mobile telecom dataset. We have also
addressed the issue of class imbalance problem, asa skewed distribution of churner percentage plays amajor role in the quality of the results.
1. Introduction
Churn is the buzzword in today’s telecom scenario.
With emergence of high end technologies and asaturated market, the onus is heavily on retaining
existing customers i.e. see to it that they don’t
churn out. The mobile network operators are
struggling to comprehend their customer problems
and in that respect they are facing difficulty inunderstanding them accurately and that timely
knowledge translates into improved businessperformances. The aim of this study is to show an
improvement on an existing methodology which
involved a combination hybrid paradigms andinfluence analysis for the prediction of churners in
a network. We will be showcasing the modification
to the data mining phase of the model by
evaluating it on a customer data of a SouthAmerican (SA) operator.
By identifying the core set of possible churners we
can deduce the factors common to their usage
pattern. This turns out to be a more than a usefulspin off. Due to the complexity in prediction anddifficulty in building a good single model thatshows high performances, many researchers have
employed a hybrid model approach in solving the
problem [2, 3, 4]. Our previous model used a
combination of tree induction with genetic
algorithms in the data mining phase, whose output
was used to seed the community based churnpropagation values [1]. The community churnvalue is described as which describes how“influenced” or “liable to churn” the node is dueto the effect of his/her neighbor’s behavioral
patterns. We will not delve into the community
churn part as it’s covered in detail [1].
2. Data Preprocessing
In a typical billing system of a mobile operator, forevery operation performed by the customer varyfrom Voice, SMS usage to GPRS usage and eachindividual events are recorded and stored. These
records are referred to as Call Detail Records(CDRs). We have considered around 0.5 million
subscriber’s CDRs over a 6 month period from a
SA telecom operator for our analysis. From theavailable users dataset 0.3 million is taken for
training and the remaining as a test set. The
required fields from CDRs are aggregated overtime intervals (in our case month, week and day
level) and possible churners are identified. In
earlier churn prediction models the fields were
aggregated over entire time period [5, 6]. The
disadvantages of other approaches is that in case of
new user who has joined in later part of the month
may be classified as churner due to his low usage
and also it’s difficult to generate any patterns fromthose data, as differences in usage pattern cannot
be derived.

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

105

These issues are analyzed thoroughly in our
approach. For example, if we have data for amonth, then we can split into 4 weekly intervals.
Now we’ll have number of calls split into 4 equalintervals. If he is a new user (who has joined in theend of month) the number of calls made by him in
the first intervals might be zero. In other models hewill be classified as a churner but in our model thatuser will not be considered as a churner. We havethe data whether a person has churned out or not asclassified by the operator over the time period. We
have attributes related to usage, spend, refill andinterconnection. We reduce our attribute list intominimal level with the help of derived gain score.
In this case, we have used an in house Hadoop [7]based data processing engine to preprocess andanalyze the bulk of the data for quicker turnaroundtime.
3. Hybrid Paradigm
A. Existing Hybrid Methodology

observed statistics of this method is presented asfollows by using WEKA implementations [9].
Results:
Correctly Classified Instances 139545
Incorrectly Classified Instances 20589
Mean absolute error
0.1286TPFP
Precisi
Recall
Rate
Rate
on
0.999
0.167 0.855
0.999
0.087
0.004 0.632
0.087

83.7268 %12.3534 %
ROC
Area
0.916
0.541

Class
0
1

We see that the same hybrid model which worked
well in the previous African operator’s dataset fails
miserably with this data. Very few churners aredetected which makes the usefulness of the model
questionable to our purpose even though it picks
the non churners properly.
B. Proposed Methodology
Classifier1

Train
DB

Data
Preprocessing

Hybrid Model

Test
DB

Classifier2
Campaign
Management

Churnerrecommendation tooperator

Predictions

Influence Score
metricFig 2. Proposed Framework for Churn Prediction

Fig.1 Hybrid Approach for Community effect of churn

In our earlier paper [1], we have used a
combination of C4.5 and Genetic Algorithm topredict the churn score. Now we applied the same
hybrid approach on the current split aggregateddata. We will be comparing the various
methodologies as we proceed in the paper. The
churn score [1] is defined as ‘the accuracy of theprediction that a person will churn out with’.
ChurnScore =( OPC4.5 + OPGA) /2
OPC4.5 = output(C4.5)*Accuracy of C4.5
OPGA= output(GA)*Accuracy of GA
The churn score is a function of the number oftimes an instance has been classified as a particularclass and the hit-rate for that class in that individualmodel. The community churn phase used a Game
Theoretic Centrality [8] concept to model the social
spread of churn [1]. This paper does not focus on
the community aspect instead our main intention isto present a strong data mining phase to the churnprediction issue, which we felt could enhance andfulfill the prediction phase of our approach. The

We now experiment with a different ensemble ofclassifiers along with the voting system of thehybrid mechanism. We take out the complexity of
the community churn part from the base model anduse a simpler influence score metric for simple
priority based recommendation mechanism as
described in Section 4. As it is observed frombelow, we keep the C4.5 algorithm (classifier 1)
constant in our experiments. This is because while
the results are important to the operator, theexplanation of the results also plays a big part i.e.
using the decision trees/if-then rules generated by
the latter, we are able to derive some conclusions
about the data and see which combination offactors plays a part in predicting customer churn.
Classifier 2 has been changed to SVM [10] andRandom Forest for comparison.
C4.5 and SVM
We now use the combination of SVM and C4.5.
Results:
Correctly Classified Instances
Incorrectly Classified Instances
Kappa statistic
Mean absolute error
TP
FP Rate Precision
Rate
0.965 0.772
0.861

13971884.1371 %
26341
15.86290.25280.2358
Recall ROC
Class
Area
0.965
0.655
0

0.228

0.228

0.035

0.564

0.655

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

1106The above combination does not give us great
results but is relatively better to the genetic
programming approach. The time taken for
execution for the SVM approach is also very highwhich is not favorable for production
environments.
C4.5 and Random Forest
We now use a combination of Random Forest andC4.5.
Results======
Correctly Classified Instances
146162
Incorrectly Classified Instances 19898
Kappa statistic
0.399
Mean absolute error
0.1259
TP
Rate
0.962

FP
Rate
0.628

Precision

Recall

0.905

0.372

0.038

0.609

87.697 %11.9388 %

0.962

ROC
Area
0.714

Class
0

0.372

0.712

1

We see the improvement in our approachsignificantly in this combination. The nonchurner’s have been classified satisfactorily while
getting a decent hit rate for the churners too. This
performs consistently on other data sets too,
namely the African operators dataset. For thefurther improvement of our model, we undertakethe class imbalance problem in the hybrid
approach.

Results:
Correctly Classified Instances
Incorrectly Classified Instances
Kappa statistic
Mean absolute error

1034672953
0.93550.0529

TP
Rate
0.998

FP
Rate
0.082

Precision

Recall

0.962

0.918

0.002

0.996

97.2251 %2.7749 %

0.998

ROC
Area
0.961

Class
0

0.918

0.961

1

We see that the hit rate with respect to the churners
has shown a huge increase by handling the classimbalance in the dataset. Our main concern is the
hit rate for the churners as we want to target as
many possible churners as possible. But
theoretically speaking if we extended the targeting
list for retention campaigns as far as the entire
network, we would have chance to understandevery possible churner. This is not a sensible
approach as such a prediction list would involve a
huge proportion of misclassified non churners
which is undesirable. Hence we would want tomaintain a good ratio between the hit rate ofchurners and the misclassification of non churners.
In terms of the ROC curve we would want a modelthat comes near the left hand side of the graph. The
reason we stress on the misclassification rate ofnon churners is that, the operator would be giving
an unnecessary discounted rate/offer to themisclassified customer which would lead to
unnecessary drop in revenue with respect to those
subscribers.

Handling Class imbalance problem

ROC
1
With
Classimbalance

0.98
0.96
0.94

TPR

The percentages we get with respect to the churnlabel seem to be less impressive mainly due to theskew in the class distribution. With an averagemonthly churn rate of 3%, the model is trained on aheavily skewed data, hence the results. We havearound 46,000 churners out of the 0.3 million
subscribers in the training set. We now split (notequal parts) the training set into 3 parts of each
0.11 million such that each and every one of thepartitions includes the 46,000 churners in them. So
now the percentage split in the training set which
was 13% churners has been increased to around39%. The final hybrid approach is applied on allthese partitions to build the model and the results
which are combined to give a prediction on the test
set. We get vector of 6 predictions from the modelsof each partition (results based on two different
classifiers). We use a voting mechanism based on
the hit rates of the predicted class for every
partition’s model and decide the final class label
for us to output as the prediction of the hybrid
approach.

0.92
0.9

RF and J48
GP and J48

0.88
0.86
SVM and J48
0.84
0.82
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FPR

Fig. 3. ROC chart for FPR verses TPR
4. Influence Score
While we are not interested to go into community
effects in churn as mentioned before, we do need asimple way of selecting subscribers from our
prediction list for us to target using campaigns to
try and retain them. While it is arguable that thechurn score itself is a good parameter to use as aselection parameter, we need to select thesubscribers in order of their influence or in otherwords target those who might affect those aroundthem.
For that we devised a function (it is normalized)

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

107Influence_score = f(no. of incoming calls, no. of
outgoing calls, total no. of distinct connections)
We take the top 69000 possible churners based on
the churn score and distribute them based on thecalculated influence score using the function
above. We clustered the subscribers based on the 6month usage statistics using K-Means clustering
methodology [9] into High, Medium and Low
usage clusters. Now we cross referenced theinfluence scores with the cluster segments asshown below. Depending upon the requirements of
the operator’s campaign management they cantarget specific risk prone users with appropriate
campaigns. This is a less expensive priority/needbased selection on a practical basis for possible
churner prediction without having to go throughthe complex graph and centrality measurecalculation in the previous paper[1]. But by nomeans is this a replacement to the effectiveness ofthe community churn methodology.
Average Influence Score based on Segments
High

Medium

Low

Max

12.39

10.01

12.97

Min

0.21

0.08

0.00034

Avg

2.25

1.24

0.54

Number of predicted Churners
Min - Avg

Avg - Max

High

2624

1914

Medium

12486

9178

Low

25041

17757

5. Conclusion
In this paper we have presented some extensions to
our earlier work on churn prediction especially inthe data mining aspect. The hybrid paradigm we
had adopted in the previous study turned out to beineffective when we used it for analysis on the newdataset. We explored new combinations andevaluated it on different datasets to see theireffectiveness in predicting churners. While keeping
the C4.5 decision tree algorithm constant is backed
by the rule based output; the genetic programmingclassifier failed miserably on the current dataset.
While the SVM classifier did relatively well, theRandom Forest classifier performed exceedingly
well with respect to predicting churners. In order toimprove the churner prediction we took care of theclass imbalance problem by splitting the data into
smaller splits with an improved churner-nonchurner distribution and used the hybridmechanisms on it. The results were very good andended up predicting most of the churners while
keeping the low misclassification rate. In the end
we used an influence score metric which was
calculated using the connection statistics of thesubscribers to use as a selection criteria to pick themost important predictions to use for targeted
campaigns. This is highly useful in production
environments as it is a less expensive operation
than using social network analysis etc.
References[1] Yeshwanth V, Vimal Raj A, and Saravanan, M.
“Evolutionary Churn Prediction in Mobile Networks using
Hybrid Learning”, in Proceedings of 24th International
Florida Artificial Intelligence Research Society
Conference (FLAIRS-24), May 18-20, 2011, Palm Beach,
Florida, USA.
[2] Wai-Ho Au Chan, K.C.C and Xin Yao. 2003. A novel
evolutionary data mining algorithm with applications tochurn prediction. IEEE Transactions on Evolutionary
Computation, 7, .532-545.
[3] Bala, J, Huang, H. Vafaie, K. DeJong and H.Wechsler.
1995. Hybrid Learning Using Genetic Algorithms andDecision Trees for Pattern Classification, Proceedings ofthe International Joint Conference on Artificial
Intelligence (IJCAI), August 19-25, 1995, Montreal,
Canada[4] Jae Sik Lee and Jin Chun Lee. 2006. Customer churnprediction by hybrid model, Advanced Data Mining andApplications, 4093, 959-966, Springer Berlin.
[5] Koustuv Dasgupta, Rahul Singh, Balaji Viswanathan,
Dipanjan Chakraborty, Sougata Mukherjea, Amit Anil
Nanavati, and Anupam Joshi. 2008. Social ties and theirrelevance to churn in mobile telecom networks. EDBT
2008: Proceedings of the 11th international conference on
Extending database technology, 668-677, New York,USA. ACM[6] Shin-Yuan Hung, David C. Yen, and Hsiu-Yu Wang. 2006.
Applying data mining to telecom churn management.
Expert System Applications, 31(3), 515-524.
[7] http://hadoop.apache.org/[8] Aadithya, K. V. and Ravindran, B. 2010. Game Theoretic
Network Centrality: Exact Formulas and Efficient
Algorithms". Proceedings of the Ninth International
Conference on Autonomous Agents and Multiagent
Systems (AAMAS 2010).
[9] Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard
Pfahringer, Peter Reutemann, and Ian H. Witten. 2009.
The WEKA Data Mining Software: An Update; SIGKDD
Explorations, 11. 1.
[10] Archaux C. And Laanaya H. and Martin A. and Khenchaf
A. 2004. An SVM based Churn Detector in Prepaid Mobile
Telephony", Proceedings of the International Conferenceon Information & Communication Technologies: from
Theory to Applications (ICTTA), Damascus, Syria, 19-23(April 2004).

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

108

Relational Learning for Customer Churn Prediction: The Complementarity of
Networked and Non-Networked Classifiers
Wouter Verbekea , Thomas Verbraken∗,a , David Martensb , Bart Baesensa,c,d
a Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven,Naamsestraat 69, B-3000 Leuven, Belgiumb Faculty of Applied Economics, University of Antwerp, Prinsstraat 13, 200 Antwerp, Belgiumc School of Management, University of Southampton, Highfield Southampton, SO17 1BJ, United Kingdom
d Vlerick, Leuven-Gent Management School, Reep 1, B-9000 Gent, Belgium

Abstract
This study examines the applicability of relational classification algorithms for customer churn prediction in the telcoindustry, and the existence and usability of non-Markovian social network effects. A range of new and adapted techniques
are proposed which are designed to handle the massive size of the call graph, the time dimension, and the skewed class
distribution present in a customer churn prediction setting. The proposed techniques are experimentally tested on alarge-scale, real life telco data set containing both networked (call detail records data) and non-networked (customer
related) information about millions of subscribers. The results indicate the existence of a limited yet highly relevant
impact of social network effects on customer churn behavior, including non-Markovian network effects. A parallel setup to
combine the output of a relational and non-relational churn prediction model leads to substantially improved performanceand boosts the generated profits.

1. Introduction

from a graph or network, as opposed to non-relationalclassifiers which require an attribute-value representationof the data. An alternative approach to relational learning exists in aggregating the information contained withina network structure into variables or attributes, which
can then be incorporated straightforward within a nonrelational model. This approach is called featurization or
propositionalization (Kramer et al., 2001).

In recent years, vast amounts of networked data on abroad range of network processes and information flows
between interlinked entities have become available, such as
for instance calls and text messages linking telephone accounts (Dasgupta et al., 2008; Richter et al., 2010), moneytransfers connecting bank accounts (Martens and Provost,2011), and messages relating email accounts. These massive, networked data logs open new perspectives for innovative business applications (Bonchi et al., 2011) and potentially hide information that is highly valuable to companies and organizations. This information is however extremely difficult to discover due to the size and the fragmentation of the data.
Networked data present both complications and opportunities for predictive data mining. The data are patently
not independent and identically distributed, which introduces bias to learning and inference procedures (Jensen
and Neville, 2002; Macskassy and Provost, 2007). Relational learning aims to exploit the information contained
within the network structure of data instances, and to incorporate this information within a network classification
or regression model (Džeroski and Lavrač, 2001; Getoor
and Taskar, 2007). Relational classifiers learn directly

2. Data description
Telco operators collect call detail records data, whichcontain detailed logs about all the transactions made by
the customers of the operator. The basic information thatis typically contained within CDR logs are the identity ofthe sender and the receiver as well as the telco operatorof the sender and receiver, a time stamp, duration or sizeof the transaction, and the product type. Possible typesof products are voice to voice calls (VVC), text messages(SMS), multimedia messages (MMS), and other specifictelecommunication services that are offered by telco operators.
Given the information contained within CDR data, agraph can be constructed that represents the social network of the subscribers of the telco operator. This graphwill be denoted the call graph. The nodes in the call graphrepresent the customers of a telco operator, and links between customers indicate they communicated. Optionally,
the network may include customers of other telco operators as well, or a competing operator vertex bundling all the∗ Corresponding author. Tel. +32.16.326.880; Fax +32.16.326.624

Email addresses: wouter.verbeke@econ.kuleuven.be (Wouter
Verbeke), thomas.verbraken@econ.kuleuven.be (Thomas
Verbraken ), thomas.verbraken@econ.kuleuven.be (David
Martens), bart.baesens@econ.kuleuven.be (Bart Baesens)

1
NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

109customers of competing telco operators in a single node.
Call detail record data was provided by an anonymous European telco operator, and covers a period of six
months. Churn labels, indicating the exact date whencustomers churned, were available for this period and one
month prior and after. The number of subscribers in thecustomer base is 1, 925, 427, and the number of edges connecting customers in the social network derived from the
CDR data equals 7, 330, 860. The class distribution is very
skewed, which is typically the case for customer churn data
sets, with on average only 0.52% of the customers thatchurn each month. Furthermore, 58 local data attributes
were provided for the each customer in the database.

of the CI procedure the relational classifier is applied, andthe result is used as input to the next iteration.
A first approach to combine a local classifier with botha relational classifier and a collective inference procedure
in a unified setup follows as well from the definitions of the
CI algorithms; the probabilities or scores resulting from a
local classification model can be used as the initial labelsof the nodes in the network. The local model is used inthis setup to provide a first estimate of the class labelsof the nodes in the network. Subsequently, the relationalclassifier and the collective inference procedure are added
as a second model layer on top of the local classifier, with
the intention to refine and improve the results of the local
model and by using the information that is incorporated
within the network data.
Conversely, the predicted probabilities by the networkmodel can be included in the local model as an explanatory
variable. In this approach the local model constitutes asecond model layer on top of the network model. In fact,
this can be regarded as an automated propositionalization
method, leading to a network variable that is not explicitly
defined but nonetheless aggregates network information.
An alternative approach exists in combining the output scores of the relational classifier, collective inference
procedure, and local classifier by learning a meta-model.
The meta-model uses the probabilities resulting from thelocal classification model and the relational classification
model, whether or not in combination with a CI procedure, as input variables. In principle, any classification
technique could be applied to induce a meta-model on topof the relational and non-relational classification models.
Finally, the network model and the local model can beapplied in a parallel, non-integrated setup, by selecting
customers indicated to have a high probability to churneither by the local model or by the network model (or byboth models).

3. Methodology
3.1. Relational classifiers, collective inference procedures,and propositionalization
Macskassy and Provost (2007) introduced a framework
for classification in networked data. In this node-centric
framework, a full relational classifier comprises a local classifier , a pure relational or network classifier , and a collective inference (CI) procedure.
A local classifier only considers attributes that are related to the entity that is to be classified. This type ofclassifiers has extensively been studied by the data mining
community (an extensive literature overview is given by
Verbeke et al. (2011)). A relational classifier makes useof the links between entities, as well as attribute values
at linked entities, to estimate the label of a given node.
However, in general, the labels of a multitude of nodes
are unknown at the time of estimation and these nodes
may be in each others neighborhood. Therefore, an iterative collective inference procedure is employed to simultaneously discover the labels for all unknown nodes in the
network. In this study, the techniques developed by Macskassy and Provost (2007) and Dasgupta et al. (2008) havebeen implemented using sparse and parallel computation
techniques in order to be applicable on massive networks
consisting of millions of nodes.
An alternative approach to the relational classifiers andcollective inference procedures exists in transforming theinformation that is contained within a graph into a set of
network variables or attributes. These network variables
can then be used as explanatory variables by traditional
data mining techniques such as for instance logistic regression. Methods that transform a relational representationof a learning problem into a propositional, feature-based or
attribute-value representation are known as propositionalization or featurization approaches (Kramer et al., 2001).

100

90

80

Fraction of churners

70

60

50

40

30

20

10

0

0

10

20

30

40

50

60

70

80

90

100

Fraction of customers

3.2. Combining collective inference, local and relationalclassifiers

Figure 1: The fraction of the churners detected by the different network models that are not detected by the local model as a functionof the selected fraction of customers with the highest predicted probability to churn of both models.

A method to combine collective inference procedures
and relational classifiers in a uniform setup follows directlyfrom the definitions of the CI algorithms. In each iteration
2

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

110

10

0.
16

Selected fracion local model with net. vars. (%)

9
8
7
3

6

2

3

5
4
3

2

3

Selected fracion local model with net. vars. (%)

2

10

2

4

1

5

5

9
8

0.

12

0.0

8

0.

16

7
6

0.
12

0.

08

5
4 0
.04
3

0.
0.

12

08

2

0.

04

1

3

1

2

3
4
5
6
7
Selected fraction network model (%)

8

9

10

(a) Contour plot of lift curve.

1

2

3
4
5
6
7
Selected fraction network model (%)

8

9

10

(b) Contour plot total fraction of included subscribers.

Figure 2: Contour plot of a two dimensional lift curve with based on the local model with propositionalized variables (LM prop) and the
network model (NM) (left), and contour plot of the total fraction of subscribers as a function of the selected fraction of the local model andnetwork model respectively.

This in fact means that in these areas of the three dimensional lift curve the parallel model obtains the same lift as
the local model for a larger fraction of subscribers. And
thus, depending on the fractions that are selected fromeach model, the parallel model yields a higher lift thanthe local model.

4. Results
In this study, four methods of combining different typesof classifiers, described in the previous section, have been
investigated. The results of this study indicate that the
fourth approach, i.e. a non-integrated setup, proves to bepromising. The reason lies in the fact that the local modeland the relational classifier identify different customers as
likely churners, which is illustrated in Figure 1. As a result, when combining both models in a unified framework
yielding one single ranking of customers, a lower performance is reached as compared to a parallel non-integrated
approach.
Figure 2(a) shows the contour plot of a three dimensional lift curve resulting from a parallel setup, for the top
fraction of subscribers selected by the local model with
network variables on the y-axis and selected by one of the
network models on the x-axis. Some overlap exists between the two top fractions of selected subscribers, andhence combining the top 5% of subscribers selected byboth models yields in total a fraction smaller than 10%of the customer base. Figure 2(b) plots the total fractionof the customer base that is selected as a function of thefractions selected by the local and the network model.
The contour plots in Figure 2 allow to interpret the three
dimensional lift curve. Starting from the y-axis, representing the lift curve of the local model with network variables,
the iso-lift curves in Figure 2(a) roughly consist of a shorthorizontal part followed by a curve downwards and a vertical part. The iso-fraction curves in Figure 2(b) on theother hand are slanted, almost straight, lines. Hence, on
the parts of the iso-lift curves with a slope that is less steep
than the slope of the iso-fraction curves, the lift remains
constant for an increasing size of the selected top-fraction.

References
Bonchi, F., Castillo, C., Gionis, A., Jaimes, A., 2011. Social network
analysis and mining for business applications. ACM Transactions
on Intelligent Systems and Technology 2.
Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D.,
Mukherjea, S., Nanavati, A., Joshi, A., 2008. Social ties and theirrelevance to churn in mobile telecom networks. In: Proceedings ofthe 11th international conference on Extending Database Technology: Advances in database technology, EDBT ’08. pp. 697–711.
Džeroski, S., Lavrač, N., 2001. Relational Data Mining. Kluwer,
Berlin, Germany.
Getoor, L., Taskar, B., 2007. Statistical Relational Learning. MIT
Press, Cambridge, MA, USA.
Jensen, D., Neville, J., 2002. Linkage and autocorrelation cause feature selection bias in relational learning. In: Proceedings of the
19th International Conference on Machine Learning. pp. 259–266.
Kramer, S., Lavrač, N., Flach, P., 2001. Relational Data Mining.
Kluwer, Berlin, Germany, Ch. Propositionalization approaches torelational data mining, pp. 262–286.
Macskassy, S., Provost, F., 2007. Classification in networked data.
Journal of Machine Learning Research 8, 935–983.
Martens, D., Provost, F., 2011. Construction and inference of networked data in a bank setting. Working paper, to be submitted
for publication.
Richter, Y., Yom-Tov, E., Slonim, N., 2010. Predicting customer
churn in mobile networks through the analysis of social groups.
In: Proceedings of the Tenth SIAM International Conference on
Data Mining. pp. 732–741.
Verbeke, W., Dejaeger, K., Martens, D., Hur, J., Baesens, B., 2011.
New insights into churn prediction in the telco sector: a profit
driven data mining approach. Submitted for publication in European Journal of Operations Research.

3
NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

111Network neighbor effects on customer churn in cell phone networks
Pavel N. Krivitsky

Pedro M. A. Ferreira

Rahul TelangiLab, H. John Heinz III College, Carnegie Mellon University

1

Introduction

In today’s deregulated mobile communication markets, carriers experience churn rates of as high as 25%
per year, and because attracting a new customer tends to be substantially more costly than retaining an
existing one, being able to predict and head off churn is eminently important to a carrier’s profitability. An
important development in churn prediction in mobile communication networks has been to take into accountthe inherently social nature of these networks: the potential of those with whom one communicates to affectone’s propensity to churn in both causal and predictive sense. In particular, churn has been modeled as adiffusion process over the network [2]; by partitioning subscribers into groups and predicting churn based ongroup structure and their most central members [7]; and using Markov Logic Networks to take into accountthe effect of network neighbors on churn [3]. Econometric models have previously been applied to model theactions of mobile phone network customers [5] and survival analysis has been used to model customer churn[6, 1].
In this paper, we provide preliminary evidence of contagious churn, that is, the propensity of a subscriber
to churn increases after her neighbors do so. When the neighbors of a subscriber churn the subscriber’s
cost of service is likely to increase because calling outside the network is typically more expensive across all
tariff plans, which might, per si, increase the tendency to churn. Yet, we show that when the neighbors ofa subscriber churn the subscriber’s propensity to churn increases beyond the impact of her changing social
circle on costs, an effect we associate with influence and word of mouth across subscribers. To show this,we use survival analysis, with instrumental variables to alleviate endogeneity problems, over a large dataset
obtained from a European carrier covering eleven months worth of detailed data on call and SMS records.

2

Data

Our dataset comprises records of all calls and all SMS messages between August 2008 and June 2009 involving
all subscribers in a large European mobile carrier, which we will call OurNet. OurNet has roughly 4 million
active subscribers with prepaid consumer grade plans during this period. For each call, the anonymized
phone numbers of the caller and the callee, the day and time of the call, its duration in seconds, and theinitial cell towers used by OurNet to service the call are available. For each SMS message, the anonymized
phone numbers of the sender and the receiver and the day and time of when the SMS was sent are. In
addition, although the numbers are anonymized, their country and area/carrier code are available. For mostsubscribers, postal code of their billing address has been recorded, and for some, age and gender as well. For
all subscribers, we have the history of their tariff plans and add-on purchases going back to 2003. For themost used tariff plans we have information on the price of both the first minute and of subsequent minutes
to both other OurNet subscribers and subscribers of other carriers. Add-ons include a number of products,
such as caller ring back tones, free calls at night or during the weekend and packs of SMS.
Over the 334 days for which we have data, 3.2 billion calls were placed for a combined 13.1 thousand
years of airtime. Roughly 14.8 billion SMS were observed during this time period. OurNet subscribers haveinitiated 2.5 billion calls, 1.9 billion of them to other OurNet subscribers. OurNet subscribers have sent
approximately 14.2 billion SMS, 13.6 billion to other OurNet subscribers. The disparity between on-net and1

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

112off-net traffic is clear and staggering. If subscribers are called “neighbors” if they hold a call or exchange
an SMS within the same calendar month, then, based on the same sample of 100,000 prepaid subscribers,
an OurNet subscriber has, on average, 15.23 neighbors. If two subscribers are only considered neighbors ifthey exchange at least one SMS in each direction within the same calendar month, then this number reduces
to 3.99; and if they are neighbors only if they call each other at least 3 (5) times within the same calendar
month, then this number reduces to 3.41 (2.11). These later definitions of neighbors capture better the ideaof a persistent relationship. The numbers of neighbors within OurNet and outside are statistically different,
the former being always higher.
The only indication that a prepaid subscriber had churned is prolonged absence of activity. OutNet
considers a subscriber churned when he or she is inactive (not placing calls or sending SMS) for at least 3
months in a row, and we follow this definition in our study (the number of prepaid subscribers who reactivate
after 3 months of inactivity is negligible. Churn is significant and appears to increase over time for OurNet.
Between September 2008 and April 2009, about 17% of OurNet subscribers left the network. The monthly
probability of churn for a subscriber that spends more time talking to other OurNet subscribers is 0.016.
This increases to 0.021 for the subscribers that spend more time talking to subscribers in other networks.

3

Methodology

Although our data only contain voice and SMS records for an eleven-month period, they also contain pricing
plan records for a much longer period. In particular, for each OurNet subscriber active in the period ofinterest, the initial subscription time is known, but the churn time is only known for those subscribers who
had churned during the period of observation and is not known for those who did not. Our information abouttime from subscription to churn is thus right-censored. This makes survival analysis a natural approach tomodeling churn. In our application, the predictors of interest, such as the fraction of neighbors who had
churned, vary over time, and because there is likely to be inhomogeneity among the subscribers, we use afrailty model with both time-constant and time-varying covariates. [4, pp. 296–308] In short, the hazard ofsubscriber i churning t months after subscribing is modeled ast−1h(t, xi , xt−1
, α, β) = zi h(t)exi ·α+xi
i·βwhere zi are Gamma-distributed individual subscriber effects, xi are the non-time-varying covariates forsubscriber i, xti are i’s time-varying covariates t months after subscriber i’s initial subscription, α and β
are parameters. The main focus of the analysis in this paper is the effect of network neighborhood on thepropensity to churn. Thus, our main predictors of interest include the fraction of airtime a subscriber spends
speaking with subscribers outside OurNet, the fraction of SMS exchanged with subscribers outside OurNet,
and the fraction of a subscriber’s neighbors, weighted by airtime and number of SMS, who had churned in
the previous month. To control for other factors likely to affect a subscriber’s propensity to churn — such theoverall level of usage — we also include tariff plan information, linear and quadratic effects of total airtime
and of number of SMS sent and received, and a rough estimate of the total cost of service, derived from usage
and the information available on tariff plans. Finally, note that those subscribers who have churned before
the period of observation are, effectively, left-truncated. [4, p. 228] This does not preclude our analysis, but
it does make it more sensitive to the proportionality of hazard assumptions.
Yet, endogeneity concerns may lead to inconsistent estimates for α and β in the model above. Consider
two neighbors, subscribers i and j that churn from OurNet. We do not know who influenced whom to churnand the fact that subscriber j might have churned before subscriber i is just weak evidence that it was him
who induced subscriber i to churn. In other words, we need to instrument subscriber j’s decision to churn.
We postulate that subscriber j churned because she was influenced to do so by her neighbors, call them
subscribers k. Therefore, we relate the decision of subscribers k to churn to subscriber j’s decision to do so,
but we would like to avoid the former to have an effect on subscriber i’s decision to churn. To this end, we
use only those subscribers k that are not neighbors of subscriber i. Clearly, subscribers k and subscriber i
can still influence each other through common neighbors other than subscriber j, but these would certainly
be second other effects (which we intent to control for in future versions of our work) with less impact.
2

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4

Results and Discussion

The table below reports preliminary results from our 2SLS estimation. Columns 1 and 2 show the first stage
results. The instruments work as expected and are highly statistically significant. An increase in the numberof the neighbor’s neighbors (subscribers k) who churn, weighted by either airtime or SMS, leads to more
churn of the neighbors (subscribers j), as indicated by the first two rows in these columns. Column 3 inthe same table shows the results of the second stage. An increase in the number of neighbors who churn(subscribers j)in the previous month, weighted by airtime, leads to an increase in the propensity to churn,
as indicated by the last two rows in column 3. This is highly statistically significant. Likewise for weighting
by number of SMS, though in this case only at the 5% level.
Subscribers without airtime or SMS exhibit a higher propensity to churn. More airtime or SMS tooutside OurNet increase the propensity to churn, though the former is not statistically significant. The costof service is not statistically significant in our regression, maybe due to multi-colinearity with usage or justbecause of inaccuracy in estimating costs. The linear and quadratic effects on airtime and number of SMS
show that, on average, subscribers involved in more calls tend to churn less, whereas subscribers involved in
more SMS tend to churn more. This difference is worth analyzing further, namely the extent it might beconfounding with age given that younger subscribers use more SMS and are more price sensitive thus more
likely to churn. Finally, having a pricing plan without a mandatory monthly contribution to the prepaid
account significantly increases the tendency to churn. In these regressions, we added monthly dummies tocontrol for seasonal effects and in the first stage we used a polynomial on the time with the company tocapture the effect of the baseline churning hazard.
In sum, in this paper we observe evidence of contagious churn, that is, when a subscriber leaves OurNet
her neighbors exhibit an increased tendency to churn too. We show that this is the case after controlling forthe fraction of airtime and SMS with subscribers outside OurNet, so our result holds beyond the fact thatwhen a subscriber’s neighbors churn the percentage of communication to outside the network also changes,
which already affects, per si, the subscriber’s decision to churn. Leveraging our 2SLS approach, which isbased on the latent structure of the social network across subscribers, we associate this effect to influence
and word of mouth across users. We are currently working on a structural model whereby subscribers choose
networks from time to time to maximize their utility and conditional on carrier choice they then call andsend messages to other subscribers. We hope to integrate soon this structural model with the reduced form
estimations presented in this paper into a unified coherent framework able to explain the neighboring effects
on churn over cell phone networks.

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%	
  airtime-­‐weighted	
  neighbors'	
  neighbors	
  churned%	  SMS-­‐weighted	
  neighbors'	
  neighbors	
  churned%	
  airtime	
  outside	
  OurNet%	  SMS	
  outside	
  OurNet
Airtime	
  (linear)
Airtime	
  (quadratic)SMS	
  (linear)SMS	  (quadratic)No	
  airtime
No	
  SMS
Costs
No	
  mandatory	
  topup
Intercept
Poly	
  months	
  subscribedMonthly	
  dummies

1st	
  Stage
Airtime1st	  Stage
SMS

0.2220***(113.52)
0.0648***(29.73)0.0750***(41.05)0.1710***(83.91)0.0025***(8.37)
0.0039***(13.18)-­‐0.4855***(-­‐6.89)
0.6190***(10.04)0.4403***(6.55)-­‐0.3746***(-­‐5.84)-­‐0.0072***(-­‐23.39)
0.0021***(8.22)
0.0000***(8.68)
0.0021***(10.83)0.0044***(16.88)yes
yes

0.0028***(10.16)-­‐0.0032***(-­‐11.50)-­‐0.1922***(-­‐2.92)
0.4343***(7.54)
0.0468(0.75)-­‐0.0970(-­‐1.62)
0.0028***(10.16)-­‐0.0080(-­‐33.00)
0.0000***-­‐7.03
0.0026***(14.55)0.0040***(16.64)yesyes%	
  airtime-­‐weighted	
  neighbors	
  churned

2nd	
  StageProb	
  Churn0.0312(1.10)
0.1204***(4.07)-­‐81.4432***(-­‐7.38)
32.9145***(11.26)21.9607***(2.58)
13.2095**(2.19)
0.6025***(25.40)0.6609***(27.40)0(0.01)
0.6439(28.82)yes
3.974***(4.98)
2.1047**(1.92)%	  SMS-­‐weighted	
  neighbors	
  churned

Results	
  from	  Survival	
  Analysis	
  with	  IV.	
  Significance	
  Levels:	
  ***	
  1%	
  **	
  5%

4

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References[1] R. N. Bolton. A dynamic model of the duration of the customer’s relationship with a continuous service
provider: The role of satisfaction. Marketing Science, 17(1):45–65, 1998.
[2] K. Dasgupta, R. Singh, B. Viswanathan, D. Chakraborty, S. Mukherjea, A. A. Nanavati, and A. Joshi.
Social ties and their relevance to churn in mobile telecom networks. In EDBT ’08: Proceedings of the
11th international conference on Extending database technology, pages 668–677, New York, NY, USA,2008. ACM.
[3] T. Dierkes, M. Bichler, and R. Krishnan. Estimating the effect of word of mouth on churn and crossbuying in the mobile phone market with markov logic networks. Decision Support Systems, In Press,
Corrected Proof, 2011.
[4] D. W. Hosmer, S. Lemeshow, and S. May. Applied Survival Analysis: Regression Modeling of Time-toEvent Data. Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., 2nd edition, 2008.
[5] Y. Kim, R. Telang, W. B. Vogt, and R. Krishnan. An empirical analysis of mobile voice service and sms:
A structural model. Management Science, 56:234–252, Feb. 2010.
[6] B. Larivière and D. Van den Poel. Investigating the role of product features in preventing customer
churn, by using survival analysis and choice modeling: The case of financial services. Expert Systems
with Applications, 27(2):277–285, Aug. 2004.
[7] Y. Richter, E. Yom-Tov, and N. Slonim. Predicting customer churn in mobile networks through analysis
of social groups. In Proceedings of the 2010 SIAM International Conference on Data Mining (SDM 2010),
2010.

5

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116Subscriber Behaviour in a Cellular Network
implementing Dynamic Pricing for Voice Calls
Han Wang, Liam Kilmartin
Electrical & Electronic Engineering,
National University of Ireland, Galway, Irelandh.wang1@nuigalway.ie, liam.kilmartin@nuigalway.ie

Abstract— In this paper we present an analysis of a large Call
Detail Record (CDR) data set gathered from a cellular
network implementing a novel Dynamic Pricing Service (DPS).
In the DPS, the tariff applied to voice calls is discounted
depending on time of day and cell location in a directedmanner to typically increase or decrease subscriber calls forrevenue or traffic load reasons. We have examined the CDRs
to determine the subscriber calling and mobility
characteristics with a view towards modelling this behaviour
and in order to examine whether there is significant evidenceof subscriber’s attempt to exploit the discounting algorithm
by means of a behaviour we have termed discount chasing.

I. INTRODUCTION
Recent years have seen significant research interest in
the analysis of records relating to the activity of subscribersof cellular networks as a means of providing insight into
subscribers’ daily behaviour, their mobility patterns andtheir social interactions. A typical mobile phone network
will process several million users’ phone calls every day
and, with such high volumes of phone calls and othersubscriber activity such as SMS, multimedia messaging
and data usage, the daily datasets generated by these
individual subscriber interactions with the network areextremely large. In this study we are specifically focussed
on the analysis of Call Detail Records (CDRs) generated bya specific entity in a mobile network in an African country
which is responsible for implementing an opt-in real-time
Dynamic Pricing Service [1-3] for voice calls made by
prepaid subscribers in that network.

discount rate in the cell. The system from which our CDRs
are taken processes all call attempts made by subscribers
using the service and confirms the discount being applied
to each individual call by means of a USSD notification
sent to the caller’s handset during the call set-up. The
rationale from a network operator’s perspective of using
such a real time DPS is to maximise cell utilisation orrevenue by offering discount in order to encourage larger
volumes of calls from the subscriber base. Hence, it iscommon, as in this case, that the discount on offer will bevaried throughout the day in each individual cell based on
current traffic load being experienced in that cell. Anothersignificant motivator for the utilisation of a DPS is as aflexible admission control strategy which attempts todiscourage users from accessing the network under heavy
load conditions by offering little or no discount andencouraging them to utilise the network services during
light load periods by offering significant discounts at thosetimes.
The data set which has been analysed in this paper
consists of a record of every call attempt made in the
network by “opted in” prepaid subscribers on a given weekday (i.e. Wednesday) over a period of several months
during which time the DPS was active. On any given day
there would typically be several million such call attempts
in the data set which was analysed. There are several aims
to this initial study:
1.

In the Dynamic Pricing Service (DPS), subscribers who
opt into the service are offered a discount on the tariff
applied to each call. This discount varies continuously
during the day and it is also dependent upon in which cellin the network the subscriber is currently located. All
subscribers in a cell are aware of the discount currently on
offer to users of the service by means of a Cell Broadcast
service notification which is used to advertise the current

2.

3.

To obtain a general understanding of the nature of
the subscriber behaviour in the network during thestudy time period.
To analyse whether there is significantly different
caller behaviour depending on the level of
discount on offer to the subscriber.
To determine whether there is a significant levelof “discount chasing” visible in the network.
Anecdotal reports from the network operator

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117suggested the growth of “micro-businesses” which
involved individuals moving from cell to cell
during the day “chasing” the largest discount onoffer and then selling on calls from their handsets
to others in their physical vicinity at that time.
II. DATASET DESCRIPTION
The raw data (CDRs) analysed in this research were
downloaded from the DPS platform for 19 weeks. The
records for each Wednesday were downloaded startingfrom 28/04/2010 and ending on 22/09/2010. Typically the
daily CDRs generated by the system represented
approximately 6.5 million call attempts involving 2 million
unique participants. A subset of the CDRs were analysed
namely those which were call attempts to other prepaid
subscribers of the same network (i.e. on-net calls). Thistypically consisted of CDRs representing an average of 3.5
million call attempts between approximately 800,000
unique participants each day. An example of the format ofan annonymised CDRs is shown in Table 1.
Time
Stamp

734263

CallerID

CalledID

Cell ID

Discount

Cell
Utilization

1

2

265

70

0.2365

Table 1: Example of individual CDRs

the daily modal discount range did vary slightly each day.
However, the periods of high traffic intensity wereconsistent and they happened in the morning between 7am
and 8am and at night between 8pm and 9pm.
B. Call and Mobility Distributions
As has been reported in other studies [4-5] with suchlarge dataset, the probability distribution function (PDF) of
call attempts in this data set does appear to exhibit
characteristics of a “power-law like” distribution. In
addition, the mobility pattern of subscribers (which wehave estimated by computing the probability that
subscriber will make call attempts for multiple cell sites
during the 24 hour period) also shows a similar “power-law
like” distribution. Figure 2 shows the resultant distributionsof call attempts and visited cells on a log-log scale.

Figure 2: Distribution of call attempts (Left) and visited
cells (Right) on a log-log scale

III. RESULTS
A. Traffic Periods in Mobile Network
Firstly, in order to get an overview of the general traffic
patterns in the network, we examined the distribution of
number of calls within 10 discrete discount ranges (i.e. 010%, 10-20%, etc.) over the day. Figure 1 is the callattempt intensity map for date 05/05/2010 where thenumber of calls was calculated during each 30 minutes
time interval during the day.

Figure 1: Example of the daily call attempt distribution

The CDRs analysed in this research come from a
dynamic pricing mobile network which means that thesubscriber who makes a phone call will be assigned a
dynamic discount rate for each call, this discount being
calculated in real time based on the cell utilization at thattime. The inclusion of this “pricing” information gives us
the opportunity to obtain the average discount for each
subscriber and then to utilize this average discount toseparate the total subscriber base into 10 sub-groups (basedon the average discount which they obtained during theday). Figure 3 is an example plot showing the distributionsof call attempts and visited cells for subscribers with anaverage discount of between 50% and 60%.

Figure 3: Power Law fit to the distribution of call attempts(Left) and visited cells (Right) in log-log scale(Discount Range: 50-60%)

The same type of analysis was completed for theremaining 18 days in the dataset and the results show that

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As we have seen, the power law does not fit the
distribution very well over its complete range. Therefore, alognormal distribution was examined as a possible better fit
for the above two distributions, as such an approach was
also successfully applied in [6]. The optimal lognormal fit
to the distribution of call attempts is shown in Figure 4(Left). This appears to offer a substantially better fit acrossthe complete range compared to the power-law fit.
Similarly, Figure 4 (Right) is the lognormal fit applied tothe distribution of number of visited cells.

Figure 4: Lognormal fit to the distribution of callattempts (Left) and visited cells (Right) in log-log scale(Discount Range: 50-60%)
With the new fitting method, the fitted lognormal
parameters, namely mean and standard deviation are
examined based on different average discount range acrossall 19 days. Figure 5 illustrates the variation of theseparameters distributions across the discount range as
determined by estimating these two parameters for each
discount range on each of the 19 days under investigation.
This analysis does show that there appears to be a relatively
well behaved and predictable variation in the lognormalmean and standard deviation versus the average dynamic
pricing discount.

Figure 5: Variation of mean and standard deviation of
log-normal fit for call attempts and visited cells distribution
In order to investigate the prevalence of discount chasingin the network, a subscriber mobility analysis wasundertaken. In this analysis, the joint calling and mobility
pattern was examined (i.e. an investigation of whether
highly mobile subscribers make more or fewer calls thanstatic subscribers). Figure 6 shows a sample density
contour map which reflects the number of subscribers
making X calls from Y cells (locations) during one specificday.

Figure 6: Joint calling and mobility pattern density
contour map
Figure 6 illustrates that most of the subscribers visited
fewer than 6 cells and that they usually made fewer than 6calls per day. The higher mobility subscribers tended tomake more phone calls and it reached the peak at about 32
calls with 17 cells visited. Above that level of usage, thesubscribers tended to be more static with for example thesubscribers who made 140 calls per day typically only
visiting fewer than 6 cells. This is not surprising sincehighly mobile subscribers tend to have occupations
requiring contacts with multiple individuals every day (e.g.
business people). Overall, on examining this behaviour
over the 19 days under study, we found that this joint
mobility\calling pattern analysis produced consistent
results and within these there was little evidence to support
any widespread discount chasing behaviour in significant
numbers in the subscriber base of the service.
C. Subscriber Calling Behaviour
As the data we analysed consists of 19 different days of
CDRs, this can be used to categorise the subscribers into 19
groups based on the number of days they appears in the
total of 19 days. This measure provides a surrogate for the
regularity with which a subscriber uses the network’s voiceservices. Figure 7 (Left) shows the average discount thatthe subscribers obtained versus the number of days during
which they made calls. The average discount obtained bysubscribers decreases as the subscribers’ regularity ofaccess increases. In other words, subscribers who makephone calls every day appear to be less concerned with thediscount offered by the operator. On a related note, figure 7(Right) shows that the subscribers who regularly makephone calls (i.e. make one or more calls on a given day)also tend to make more calls per day, with a very
distinctive change in behaviour in both plots once
subscribers access the network on 10 or more of the 19

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119days (i.e. subscribers who access the network on more than
≈53% of days).

Figure 7: Distribution of average discount versus accessregularity (Left) and distribution of average number of calls
versus access regularity (Right)
IV. CONCLUSIONS AND FUTURE WORKIn this paper, we have provided a summary of, to ourknowledge, a unique dataset containing call detail records
for subscriber voice calls made in a network implementing
real time dynamic pricing. We analysed the subscriber
calling and mobility behaviours from a general view basedon different discount ranges. The results appear to showthat there is variation in subscriber behaviour based on the
average discount which they obtain but that there does notappear to be any systematic abuse of the service throughthe behaviour of discount chasing. Further analysis of thisdata set is ongoing and focussed on determining parameterswhich would facilitate the development of a statisticalmodel for subscriber behaviour in this environment. Also,
we have recently commenced work on a graph theoretic
analysis of the dataset with the aims of identifying a meansof “individualising” the dynamic pricing strategy in amanner which will maximise its influence on the complete
subscriber base behaviour.
REFERENCES
[1]

[2]

[3]

[4]

[5]

[6]

Olivré, A., Call Admission Control and Dynamic Pricing in a
GSM/GPRS Cellular Network, in Computer Science. 2004,
University of Dublin.
Khanifar, E.D.F.-N.a.A., Dynamic pricing in mobile communication
systems, in First International Conference on 3G Mobile
Communication Technologies. 2000. p. 416-420.
Fitkov-Norris, E.D. and A. Khanifar. Dynamic pricing in cellular
networks, a mobility model with a provider-oriented approach. in 3G
Mobile Communication Technologies, 2001. Second International
Conference on (Conf. Publ. No. 477). 2001.
Gonzalez, M.C., C.A. Hidalgo, and A.-L. Barabasi, Understanding
individual human mobility patterns. Nature, 2008. 453(7196): p.
779-782.
Onnela, J.P., et al., Structure and tie strengths in mobile
communication networks. Proceedings of the National Academy of
Sciences, 2007. 104(18): p. 7332-7336.
Seshadri, M., et al., Mobile call graphs: beyond power-law andlognormal distributions, in Proceeding of the 14th ACM SIGKDD
international conference on Knowledge discovery and data mining.
2008, ACM: Las Vegas, Nevada, USA. p. 596-604.

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What is the Economic Value of Cell Phone
Location Data?
Francois Baccelli (ENS/INRIA)
Francois.Baccelli@ens.fr
http://www.di.ens.fr/˜baccelli/
Abstract—The defining characteristic of mobile and cell phoneusers is how they change location over time. Information aboutlocation is becoming critical, and therefore valuable, for an
increasingly larger number of location-based or location-aware
services. One key open question, however, is how valuable exactly
this information is.
Our goal in this work is to develop an analytic framework,
namely models and the techniques to solve them, to help quantify
the economics of location information. We consider in particular
the fundamental problem of quantifying the value of different
granularities of location information, for example how much
more valuable is it to know the GPS location of a mobile user
compared to only knowing the access point, or the cell tower,that the user is associated with. We illustrate our approach byconsidering what is arguably the quintessential location-based
service, namely proximity-based advertising or mobile coupons.
We make three main contributions. First, we find that determining the economic value of location is inseparable fromdetermining the value of user preference or user profile data.
The question then is not ”what is the value of location data”
but ”what is the relative value of location vs preference data”
or equivalently: what is more valuable - know where you are or
to know what you like? We then develop several novel models,
based on stochastic geometry, which capture the location-based
economic activity of mobile users with diverse sets of preferences
or interests and we derive closed-form analytic solutions for the
economic value generated by those users. Third, we augment
the models to consider uncertainty about the users’ location,and derive expressions for the economic value generated withdifferent granularities of location information.
To our knowledge, our work is the first one to present
and analyze economic models which can help understand theeconomic value generated by mobile users with location based
services, for different granularities of location information in
wireless networks. A detailed description of the work is available
in reference [1].

I. I NTRODUCTION
The analysis of cell phone data is a very exciting and rapidly
growing area of research because the data reveals interestingand previously hidden aspects of the behavior of cell phoneusers, and by extension of human behavior in general. Because
it reveals personal and aggregate aspects of human behavior,
the data is valuable to both the users (who would like toprotect the more personal aspects of it) and to a plethora ofservice providers such as operators of location-based software
service providers (who would like to present or advertise
well-targeted or personalized information and offers to users).
Therefore, two key questions related to cell phone data arethose of privacy and economics, namely: how revealing isJean Bolot (Technicolor)Jean.Bolot@technicolor.com
http://jeanbolot.com/
cell phone data and how can it be shared in a privacypreserving manner on one hand, and how can it be used topersonalize and improve user experiences and what is the valuethat it brings in the process on the other hand? We consider
the second question in this work. Specifically, since location
data enables new services and new economic activities, it istherefore naturally seen as economically valuable. This raises
the fundamental question then of how valuable it is, and how
to quantify that value.
Developing models to study and quantify the economic
value of location data is a very challenging task, because
the models need to capture a number of different factors
such as i) spatially distributed users, ii) spatially distributed
businesses and/or entities that trigger economic transactions,iii) transactions dependent on user location and, likely iv)
transactions dependent on specific user preferences and interests. Furthermore, the models need to capture the granularity at
which location information is available. We do not present inthis work models that capture all the properties of mobile users
involved in and responding to proximity-based advertising (forexample we do not consider non-Poisson or non-homogeneoususer distributions). Deriving and especially solving such ageneral model is still an extremely challenging problem. Thisis a first step, which presents what we believe is a useful andvery promising approach to tackle and formalize the general
problem.
We consider in this paper what is arguably the quintessential
location-based service, namely proximity-based advertising: amobile user, with a given set of preferences or interests, isoffered an advertisement (in the form of an SMS coupon, ora discount of some kind) when getting close to a business ora store which offers products and services that match (at least
partially) with the interests of the user - in practice, a hungry
user who likes Italian food would get a coupon from a pizzeria
for discounted pizza when getting within some range of thatpizzeria. Mobile coupon usage is expected to triple by 2014,
exceeding 300 million people; almost 3 billion coupons are
expected to be issued by 2011 with coupon redemption value(the amount of discounts redeemed) expected to approach $7
billions globally [2]. An economic model of this situation
helps answer the following questions• What is the overall economic value generated by suchtransactions?
• What is the additional value created by fine-granularity
location data (such as GPS data) compared to coarse-

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2

granularity data (such as cell tower or access point-leveldata)?
• The location-aware advertisement situation describedabove typically involves three parties: 1) the mobile
operator which provides or exposes the location data,
2) an entity which provides or exposes the interests orpreferences of the user (for example Google, based on
past search queries of the user), and 3) the source of thead (the restaurant). Given that the user responds to the ad
and walks into the restaurant to buy a pizza, how should
the mobile operator, Google, and the restaurant (all ofwhich provided component of the transaction) share therevenues created by the transaction?
II. D ESCRIPTION OF THE MODEL
We model the spatial distribution of businesses (such as
the pizzeria above) which offer services that might be ofinterest to the mobile users (and which therefore might leadthem to send mobile ads or coupons). We assume that there
is a denumerable number of businesses or services which
are randomly deployed in an infinite plane. We assume that
services of type n (e.g. pizzeria versus movie theater vs coffeeshop) are deployed according to a homogeneous Poisson point
process Φn of intensity λn and that all these Poisson processes
are independent (refer here and throughout the rest of the paper
to [3] for background and details on stochastic geometry). Let
us stress that the Poisson assumption is adopted for tractabilityof the analysis and that it is beyond the scope of the present
paper to validate it in statistical terms. The homogeneity
assumption should also be challenged as it is clear that urban
densities vary from city centers to suburbian environments.
There are two ways of addressing this last question. The
simplest way consists in considering the model to represent
a large but homogeneous area. The second way consists inextending the analysis to non-homogeneous Poisson point
processes, which seems feasible. This last line of thought isleft for future work.
Users are characterized by a random preference list which
is a list of services, namely a subset of the integers. Usersof class (k, i1 , . . . , ik ) have k elements in their list and theseelements are the services (i1 , . . . , ik ) = i they are interested
in. The following notation is used:
The probability to have a user of this class is denoted by
π(k, i).
• The radius of the ball defining the vicinity of a user isdenoted by R.
• The spatial density of users is denoted by ν.

•

In the basic model described here, we adopt the following
assumptions:•

The propensity for users to stop and check out theavailable services depends on the total number of services
m matching their list and located in their vicinity; this isquantified by a function f (m); it makes sens to assume
that f is non-decreasing.

Given that a user stops, the revenue generated is proportional to the number of different services located in
his/her vicinity.
The f function is central in the model in that it captures
the essence of the physical/psychological process in action:
because mobile users are localized (by the wireless network
operator), and because their interests are known (thanks toGoogle), they can be informed on how well their current
location matches their interest through the variable m (we havealso developed more elaborate scenarios based on ratings) andthis triggers (through some psychological process) a decisionof the user to check the services with the probability f (m)and eventually to purchase.
Now that the bases for the model are in place, we can
examine how to quantify the economic value generated by
the users responding to the location-aware advertisements inthis case.
Pick a typical user. Given this user is of the (k, i) type, whatmatters foris the Poisson point process of intensity
Phis/herkλ(k, i) = j=1 λij , which is that of services present in his/her
preference list.
It is easy to see that in case of a perfect localization,a user of type (k, i) could be sent a proactive message
informing him/her of the presence of m services matching
his/her preferences iff his/her location belongs to the mcoverage region of the Boolean, or germ-grain model withgerms (the points of the Poisson point process) of intensity
λ(k, i) and with grains equal to balls of radius R, centeredon these points. By definition, a location belongs to this mcoverage region if exactly m balls cover it.
Because the number of grains that cover a given point
follows a Poisson law on the integers of parameter λ(k, i)πR2 ,the probability that a typical location is m-covered is
2 m−λ(k,i)πR2 λ(k, i)πR.
p(m, k, i) = em!
Hence the mean revenue generated per unit of space underModel 1 is
XX
X
ρ = ν
π(k, i)f (m)g(m, k, i)
•

k

m

i

m
λ(k, i)πR2(1)em!
where g(m, k, i) denotes the mean number of different services
among the m for a user of type (k, i). Using the fact that theprobability that therem is no service of
Pktype p among the m is
1 − λip /λ(k, i) , with λ(k, i) = q=1 λiq , we get that
−λ(k,i)πR2g(m, k, i)

= k−

k 
X
1−
p=1

λip
λ(k, i)m

To get (1), we used: (i) the assumption that the probabilityof stopping given an m-match depends only on m through afunction that we denote by f (m); (ii) the assumption that themean revenue given an m-match is proportional to the numberof different services among the m (we take the mean revenue

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

122

3

per service given that this service is visited by the user equal
to 1).
In the special case when:• There are N types of services.
m• f (m) = 1 − αwith 0 < α < 1, where the constant−1
α is the propensity to react to proactive information.
N −1k• π(k, i) = β (1 − β) k
, i.e. we have a geometricsize for the list of preferences and a uniform law on theservices; the parameter β determines the mean size of thelist of preferences as provided by e.g. Google.
• λn = λ for all n; λ is the spatial density of services.
m , so thatthen g(m, k, i) = k 1 − 1 − k1
X
X
ρ = ν
β k (1 − β)
(1 − αm ) (k − k(1 − 1/k)m )
m

k
−λkπR

e

2

λkπR2
m!

m

or

ρ ≈ νβ1−e−λπR
1−β

2

2

−

(1−β) e−λ(1−α)πR −e−λπR(1−βe−λ(1−α)πR2 )

2



2

We also compute the mean revenue per unit space ρ0 when theuser location is not known accurately (specifically when theuser is mistakenly located at distance r from its true location)and the mean revenue per unit space ρ0 when neither the user
location nor its preference lists are known accurately.
Comparing ρ0 to ρ0 lets us quantify the value brought bya knowledge of the user preference list, and comparing ρ0 toρ lets us quantify the value brought by a knowledge of theuser location. With closed form expressions for ρ, ρ0 and ρ0 ,we are in a position to answer the question we raised at the
beginning of the paper, namely: ”What is more valuable: toknow where you are or to know what you like?”
The answer is shown in Figure 1 below. We plot the mean
revenues per unit space ρ (in red), ρ0 (in green) and ρ0 (inyellow) for comparison for three different values of λ, i.e.
for three different densities of services - low (left graph)corresponding to a rural or sparsely service environment,
medium (middle graph) and high (right graph) corresponding
to a dense urban core or a locally densely service area.

2
0.4
0.03

1.5

0.3

0.02
0.2

1

creates the largest revenue per unit space, primarily by providing the best positive correlation between the users’ decisionof stopping and their interests.
We also observe that the impact of the parameter α, thepropensities to stop, is moderate.
Finally, and most interestingly, we observe that accurate
information about location is the source of most of the revenue
in rural or sparsely serviced areas (green curve is almost
similar to yellow curve meaning profile information does not
add much value, compared to red curve meaning location adds
most of the value), but that profile or preference data adds
most of the value in densely serviced areas or urban core.
Therefore the answer to the question above is: It is better toknow where you are in rural areas, but better to knowwhat you like in urban areas.
Of course, the last figures do not have universal validity
and only hold for the chosen set of parameters. However, theysuggest a methodology to answer the question of the sharingof the revenues created by the transactions between the mobile
operator and e.g. Google: it makes sense to propose that thisrevenue be shared proportionally to the relative gains broughtby exact localization and by the list information respectively,
which in turn means that revenues should preferentially go
to the provider of user profiles rather than location in urban
environments, and vice versa in rural environments.
III. C ONCLUSION AND F UTURE W ORK
We have presented and analyzed a novel model allowing
one to capture how to jointly leverage the combination of three
types of basic information: 1) that of the geographic locationand mobility of users of a cellular phone networ,k 2) that of
their needs and interests as obtained from their preference lists
and 3) that of the services available at all locations of space
and their rating by the users. We have shown how the model
could be used to quantify the revenue of this combination of
informations and thus to quantify the economic value of cell
phone location and user preference data.
The model does not capture all the properties of ”real-life”
mobile users involved in and responding to proximity-based
advertising. Deriving and especially solving such a generalmodel is clearly a challenging area for future research, butalso an extremely important area since much of the activity
and interest around cell phone data mining is supported or motivated by economic considerations, ranging from marketing
opportunities (as in mobile proximity advertising) to societal
improvements (as in location-based traffic optimization for
example).

0.01
0.1
0.5

0

0.6

0.7

0.8
al

0.9

0 0.5

0.6

0.7

0.8
al

0.9

R EFERENCES
0.5

0.6

0.7

0.8

0.9

al

Fig. 1. Mean revenues per unit of space for three different values of λ =.01, .1 and 1[1] F. Baccelli and J. Bolot, “Modeling the economic value of location andpreference data for mobile users,” in Proc. IEEE Infocom’11, Shanghai,
China, April 2011.
[2] H. Wilcox, Mobile Coupons and NFC Smart Posters: Strategies, Applications and Forecasts 2009-2014. Juniper Research, Dec. 2009.
[3] D. Stoyan, W. Kendall, and J. Mecke, Stochastic Geometry and its
Applications. Wiley, 1995.

Not surprisingly, ρ is always higher than ρ0 or ρ0 , meaningthat knowing both the location and the preferences of users

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McCormick Hall, W4 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C,D3
New House, W70 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
		 French House . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
		 German House . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
		 iHouse House . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3
		 Spanish House . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
Next House, 500 Memorial Drive, W71 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
Random Hall, NW61 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C2
Senior House, E2 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Sidney-Pacific, NW86 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B2
Simmons Hall, W79  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
Tang Residence Hall, W84 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . A3
Warehouse, The, NW30 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C2
Westgate Apartments, W85 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
Compton Gallery, 10-1st floor . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Dean’s Gallery, E52-466  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3Hart Nautical Galleries, 5-1st floor  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
List Visual Arts Center, E15-109 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
MIT Museum, N52-2nd floor  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D2
Wiesner Student Art Gallery, W20-2nd floor .  .  .  .  .  .  .  .  .  .  .  .  . D3
Wolk Gallery, 7-338 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Music and Theater Arts Program, 4-246  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Naval Science (NROTC), W59-110 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
News Office, 11-400 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Nuclear Science and Engineering Department, 24-105 . .  .  .  .  .  .  .  . D3
Nuclear Science, Laboratory for, 26-505 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Ocean Engineering, Center for, 5-228  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Operations Research Center, E40-149 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Parking and Transportation Office, W20-022 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Physics Department, 4-304 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Picower Institute for Learning and Memory, 46-1303 .  .  .  .  .  .  .  .  . D,E2
Plasma Science and Fusion Center, NW16,17,21,22 .  .  .  .  .  .  .  .  .  .  . C2Police, Detail Office, W20-020B . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Political Science Department, E53-470 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3,4
Post Office (U.S.), W20-003 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
President’s Office, 3-208 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Procurement, Department of, NE49-4122  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D,E2
Professional Institute, MIT, 35-433 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Provost’s Office, 3-208 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Public Service Center, 4-104 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Publishing Services Bureau, E38-254 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Quarter Century Club, E19-432 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Real Estate, Center for, W31-310 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Real Estate Office, E48-2nd floor .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Reference Publications Office, E38-234 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Registrar’s Office, 5-111, 5-119 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Research Laboratory of Electronics, 36-419 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D,E2
Residential Life and Student Life Programs, W20-549 . .  .  .  .  .  .  .  .  . D3
Resource Development, W98 (600 Memorial Dr.) .  .  .  .  .  .  .  .  .  .  .  .  . A4
Sala de Puerto Rico, W20-2nd floor . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Schedules Office, 5-111 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Science, Technology, and Society, Program in, E51-185 . .  .  .  .  .  .  .  .  . F3
Sea Grant College Program, E38-300 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3Sponsored Programs, Office of, E19-750 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Student Center, W20, see separate
listing under Visitor Information .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Student Financial Services, 11-320 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Student Life, Office of the Dean for, 4-110 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Student Services Center, 11-120  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Student Support Services (counseling and support), 5-104 . .  .  .  .  . D3
Tang Center, E51 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Technology Licensing Office, NE25-230 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2,3
Technology, Policy, and Industrial
Development, Center for, E40-227 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Theater Arts, 10-274 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Tours, Campus, 7-121 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Transportation and Logistics, Center for, E40-276 .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Undergraduate Advising and Academic Programming, Office of,
7-103  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Undergraduate Education, Office of the Dean for, 7-133  .  .  .  .  .  .  . D3
Undergraduate Research Opportunities Program, 7-104 .  .  .  .  .  .  .  . D3
Urban Studies and Planning Department, 7-337 .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Video Production and Digital Technology, NE48-308  .  .  .  .  .  .  .  .  .  .  . E2

Museums and Galleries

MIT Investment Management Company, E48-200 . .  .  .  .  .  .  .  .  .  .  .  .  . F3
MIT Press, E39  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
MIT Card Services, W20-021 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
MITAC, Stata Center, 32 (1st floor)  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2,3

ATM Locations:Student Center, W20 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Stata Center, 32 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2,3
E18 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Building 10 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Tech Square, NE48  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2
Draper Lab .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2

McGovern Institute for Brain Research, 46-3160 . . . . . . . . . . . . . . E2
Mechanical Engineering Department, 3-173 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Media Arts and Sciences Program, E15-401  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3Media Lab Complex, E14  . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Medical Department, E23-189 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Minority Education Office, 4-113 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
MIT Federal Credit Union:
Student Center, W20  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D3
Tech Square, NE48  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E2

MIT campus map index (building number follows name)
Admissions Office, 3-108, 3-103  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Admissions Receptions, 10-100 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Advanced Visual Studies, Center for, N52-390 .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D2
Aeronautics and Astronautics Department, 33-207 . .  .  .  .  .  .  .  .  .  .  . D3
Aga Khan Program for Islamic Architecture, 10-390 .  .  .  .  .  .  .  .  .  .  . D3
Air Force ROTC (Aerospace Studies), W59-114 .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Alumni Association, W98, 2nd floor (600 Memorial Dr.) . .  .  .  .  .  .  . A4
Anthropology Program, 16-223 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Architecture Department, 7-337 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Army ROTC, W59-198 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Artificial Intelligence Laboratory (CSAIL), 32-G415 .  .  .  .  .  .  .  .  .  .  . E2,3
Arts, Office of the, E15-205 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Communications, Special Programs .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Council for the Arts .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3

Athletic Facilities
Alumni Pool, 57 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Briggs Field .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
du Pont Athletic Center, W32  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
du Pont Center Gymnasium, W31  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Johnson Athletics Center, W34  . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Pierce Boathouse, W8  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C4
Rockwell Cage, W33 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Sailing Pavilion, 51 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E4
Steinbrenner Stadium, West of W34 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Tennis Courts
		 J.B. Carr Indoor Tennis Center, W53 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
		 du Pont Courts, near W53 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
		 Saxon Tennis Courts .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Wang Fitness Center, Stata Center, 32 . . . . . . . . . . . . . . . . . E2,3
Zesiger Sports & Fitness Center, W35 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3

Bookstores

Audio-Visual Services, 4-017 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Banking, W20 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
ATM Machines Lobby, 10 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
W20-1st floor  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Stata Center, 1st floor . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2,3
Credit Union, NE48 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2
Bartos Theatre, E15-070 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Biological Engineering Department, 56-341 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Biology Department, 68-132 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Biotechnology Process Engineering Center, 16-429 . .  .  .  .  .  .  .  .  .  .  . E3
MIT Press Bookstore, E38-176 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Tech Coop Kendall Square  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Tech Coop (no textbooks), W20-1st floor  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Brain and Cognitive Sciences Department, 46-2005 .  .  .  .  .  .  .  .  .  .  . E2
Broad Institute, 7 Cambridge Center .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2, F1
Bush Room, 10-105 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Campus Activities Complex, W20-500 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Campus Dining Office, W20-500 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Campus Police, W89 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . A3
Campus Police/event registration, detail office, W20-022 .  .  .  .  .  . D3
Chairman of the Corporation, 5-205 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Chancellor, 10-200  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Chapel, W15 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Chaplaincy, W11 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Chemical Engineering Department, 66-350 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Chemistry Department, 18-380 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Civil and Environmental Engineering Department, 1-290  .  .  .  .  .  .  . D3
Clinical Research Center, E17-445 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Community Services Office, E19-432 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Comparative Media Studies, 14N-207 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Comparative Medicine, Division of, 16-825 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Computer Science, Laboratory for (CSAIL), 32-G415 .  .  .  .  .  .  .  .  .  . E2,3
Conference Services, 12-156 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3

Copy Technology Centers(Main Facility) 11-004  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
2-217  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
E52-045  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
W20-102 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D3

Corporate Relations – Industrial Liaison Program, W98-400  .  .  .  .  . A4
CSAIL, 32-G415 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2,3

Dining Rooms
Lobdell (Student Center), W20-2nd floor .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Forbes Family Café (Stata Center), 1st floor  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3

Dormitories
Ashdown House, NW35 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B2
Baker House, W7  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Bexley Hall, W13 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3

Walker Memorial, 50 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Wellesley Exchange Program, MIT 11-120  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Whitaker College of Health Sciences and Technology, E25-525 . . E3
Whitehead Institute for Biomedical Research,
9 Cambridge Center  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2
Women’s League, MIT, 10-342 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Women’s and Gender Studies, 14E-316 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Wong Auditorium (Tang Center), E51 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Work, Family and Personal Life, Center for, 16-151  .  .  .  .  .  .  .  .  .  .  . E3
Writing and Humanistic Studies Program, 14E-303 .  .  .  .  .  .  .  .  .  .  .  . E3

Named Buildings and Facilities

Alumni Swimming Pool, 57  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Ashdown House (Avery Allen), NW35  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B2
Baker House (Everett Moore), W7 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Bexley Hall, W13  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Briggs Field, West Campus . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B,C3
Brown Building (Stanley Gordon), 39 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D2,3
Burton-Connor, W51 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Bush Building (Vannevar), 13 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Carr Indoor Tennis Facility (J.B.), W53 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Compton Court near, 26  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Compton Laboratories (Karl Taylor), 26  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Dorrance Building (John Thompson), 16  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Dreyfoos Building (Alexander W.), 32D .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2
Dreyfus Building (Camille Edouard), 18 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
du Pont Athletic Center (David Flett), W32 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
du Pont Center Gymnasium (David Flett), W31 .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
du Pont Court, near 1  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
East Campus (Alumni Houses: Bemis, Goodale,
Hayden, Munroe, Walcott, Wood), 62, 64 .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Eastgate, E55  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Eastman Court, near 6  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Eastman Research Laboratories (George), 6  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Edgerton House (Harold E.), NW10  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C2
EG&G Education Center (Edgerton, Germeshausen and Grier), 34 . . D2,3
Fairchild Building, 36, 38  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D2
Ford Building (Horace Sayford), E18, E19 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Francis Bitter Magnet Laboratory, NW14  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C2
Gates Building (William H.), 32G .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2Gray House, Paul & Priscilla (President's House), E1  .  .  .  .  .  .  .  .  .  .  . E3
Green Building (Cecil & Ida), 54 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Green Hall (Ida Flansburgh), W5  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Guggenheim Aeronautical Laboratory (Daniel), 33  .  .  .  .  .  .  .  .  .  .  .  . D3
Hayden Memorial Library (Charles), 14 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Hermann Building (Grover M.), E53 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Homberg Building, 11 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Johnson Athletics Center (Howard W.), W34  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Killian, (James R., Jr.) Court, adjacent to Memorial Drive  .  .  .  .  .  . D3
Koch Building (David H.), 68 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Koch Institute for Integrative Cancer Research, 76 .  .  .  .  .  .  .  .  .  .  . E2,3
Kresge Auditorium (Sebastian S.), W16 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C,D3
Landau Building (Ralph), 66 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Lowell Court, near 2 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
MacGregor House (Frank S.), W61 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
Maclaurin Buildings (Richard Cockburn), 3, 10, 4  .  .  .  .  .  .  .  .  .  .  .  .  . D3
McCormick Hall (Stanley), W4 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C,D3
McDermott Court, near 54 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
McGovern Institute for Brain Research, 46-3160 . . . . . . . . . . . . . . .E2
McNair Building (Ronald E.), 37 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Muckley Building (Dwight S.), E40 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
New West Campus Houses (Ballard, Coolidge,
Desmond, Lawrence, Thorn), W70 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
Parsons Laboratory for Water Resources andHydrodynamics (Ralph M.), 48  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2
Picower Institute for Learning and Memory, 46-1303 .  .  .  .  .  .  .  .  .  .  . E2
Pierce Boathouse (Harold Whitworth), W8 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C4
Pratt School of Naval Architecture and Marine Engineering, 5 .  .  .  . D3
Random Hall, NW61 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C2
Rockwell Athletic Cage (John Arnold, M.D.), W33 .  .  .  .  .  .  .  .  .  .  .  . C2
Rogers Building (William Barton), 7 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Saxon Tennis Courts, (David S.)  . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Senior House (Atkinson, Crafts, Holman, Nichols,
Runkle, Ware), E2 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Simmons Hall, W79  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . B3
Sloan Building (Alfred P., Jr.), E52  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . F3
Sloan Laboratories, 31 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Sloan Laboratory (Alfred P.), 35  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3
Stata Center, (Ray and Maria), 32  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2,3
Steinbrenner Stadium (Henry G.) .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . C3
Stratton Student Center (Julius A.), W20  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . D3

Tang (Jack C.) Center for Management Education, E51  .  .  .  .  .  .  .  .  . F3
Tang Residence Hall, W84 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . A3
Walker Memorial (Francis Amasa), 50 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Westgate, W85 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . A,B3
Whitaker Building (Uncas A.), 56 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Whitaker College of Health Sciences and Technology(Uncas A. & Helen), E25 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Whitehead Institute for Biomedical Research,
9 Cambridge Center  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E2
Wiesner Building (Jerome B.), E15 . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E3
Wood Sailing Pavilion (Walter C.), 51 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . E4
Wright Brothers Wind Tunnel (Wilbur & Orville), 17 .  .  .  .  .  .  .  .  .  .  . D3
Zesiger Sports & Fitness Center, (Albert and Barrie), W35 .  .  .  .  .  .  . C3

Visitor information

As parking is limited and regulations are aggressively
enforced, visitors are strongly encouraged to use public
transportation.

By public transportation, MBTA (“The T”)

A parking garage at Alewife Station (Routes 2 and 16 at the
Cambridge/Belmont/Arlington line) allows access to the Red Linesubway. Take the Red Line from Alewife Station in Cambridge or fromPark Street Station in Boston to Kendall/MIT Station or to the Central
Square Station, both of which are within walking distance of MIT.
There is regular MBTA bus (#1) service along Massachusetts Avenue
from MIT to Boston and Harvard Square.

From Logan Airport by MBTA

Taxi fare is about $20~$30. To travel by subway, at Logan Airport takethe airport shuttle bus that runs to the Blue Line Subway Station.
Take the Blue Line to the Government Center Station and go upstairs
to board the Green Line Trolley to Park Street. At Park Street Station
go downstairs to board the Red Line Subway going outbound towards
Alewife and exit at Kendall/MIT or Central Square Station.

By car from Logan Airport

Leaving the airport follow the signs to the Summer Tunnel. Entering
the tunnel keep in the right lane for Storrow Drive North. Continue
in the right lane following the Storrow Drive West signs. Exiting
the tunnel you will see the Massachusetts Eye and Ear Infirmary
on your left and the Charles River on your right. Keep to the rightand continue to follow the Storrow Drive West signs. After passing
under the Arthur Fiedler pedestrian walk bridge, change to the leftlane and take exit for Massachusetts Avenue/Cambridge (2AN).
Bear right and cross the Harvard Bridge. MIT's main entrance at
77 Massachusetts Avenue will be on your right, at the third set of
traffic lights.

By car from Route I-93 or Southeast Expressway

Take exit 26. Keeping in the right lane, follow the Storrow Drive Westsigns. After passing under the Arthur Fiedler pedestrian walk bridge,change to the left lane and take exit for Massachusetts Avenue/
Cambridge (2AN). Bear right and cross the Harvard Bridge. MIT's
main entrance at 77 Massachusetts Avenue will be on your right, atthe third set of traffic lights.

By car from Route I-90

Take the Cambridge/Allston Exit (18) on the left off of the
Massachusetts Turnpike (Interstate 90). Bear right at the end of theramp and go through two sets of traffic lights. Follow the signs to
Cambridge. Cross the River Street Bridge and continue straight ahead
for about one mile to Central Square. Turn right onto Massachusetts
Avenue. MIT’s main entrance, 77 Massachusetts Avenue, is about
one-half mile down on the left.

   = MIT permit parking

By car from Route I-95 low the I-93 directions to MIT as given.
From the south take I-90 off of I-95 and follow the I-90 directions to
MIT as given.

Call a taxi

Yellow Cab; 617-492-0500= public parking (pay lots)

ParkingCampus telephones

MIT house telephones are located in many of the campus lobbies
including the Student Center (map section D). To reach an office
extension from a house telephone dial the last 5 digits (i.e. 3-4795)
of the number. There are pay telephones in the Student Center (mapsection D), as well as other locations around the campus. To contact
a person, department, or residence at MIT, dial 0 from a house phoneor 617-253-1000 from a pay telephone.

Campus tours

Tours of campus: 11:00 am and 3:00 pm weekdays except holidays.
Tours leave from 77 Massachusetts Avenue, Lobby 7 (map section D).
Admissions Office undergraduate information sessions: 10:00 am
and 2:00 pm (preceding tour) from mid-March through mid-December
except holidays. Confirm at http://web.mit.edu/admissions/.

Dining on campus

January 2010

Massachusetts Institute of Technology
Information Center
Room 7-121
Telephone 617-253-4795
http://web.mit.edu
77 Massachusetts Avenue
Cambridge, MA 02139-4307

For more information

Bank, 1st floor
Cafeteria, 2nd floor
Campus Police/event registration detail, basement
Cleaners, basement
Conor Moran Lounge, 5th floor
Copy Technology Center, 1st floor
Food Market/convenience store, 1st floor
Game Room, 1st floor
Hair salons, basement
Manager, Campus Activities Complex, 5th floor
MIT Card Services, basement
Optical Store, basement
Parking and Transportation Office, basement
Police, Detail Office, W20-020B
Post Office (U.S.), basement
Restaurants, 1st and 2nd floors
Stratton Lounge, Catherine N., 2nd and 3rd floors
Tech Coop, 1st floor (no textbooks)
Wiesner Student Art Gallery, 2nd floor

Student Center facilities
W20 - 84 Massachusetts Avenue (map section D)

The MIT Events Calendar is available online at
http://events.mit.edu.
A map giving locations of the public art in MIT's Permanent
Collection, overseen by the List Visual Arts Center, may be found at
http://web.mit.edu/lvac.
The following 24-hour numbers are available for recorded ­information
on current arts events:
Concerts 617-253-9800
List Visual Arts Center 617-253-4680
MIT Museum 617-253-4444
Theater Arts 617-253-4720

MIT events and exhibits

One of the country’s largest university presses, the MIT Press publishes books and journals circulated throughout the world. Its titles
include professional, reference, and scholarly books; graduate andundergraduate texts; and books for general audiences. The MIT Press
Bookstore is located at 292 Main Street (map section F).

The MIT Press

Snacks and meals are available in the Student Center (map sectionD) and in the Stata Center (map section E). There are restaurants andsmall eating places in the Kendall Square area of the campus and in
the local hotels adjacent to the campus.

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NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

Table of contents
Programp. 7

Abstracts session A

p. 17

Abstracts session B

p. 31

Abstracts session C

p. 45

Abstracts session D

p. 65

Abstracts session E

p. 77

Abstracts session F

p. 89

Abstracts session G

p. 103

campus map

p. 123

NetMob 2011 - Second conference on the analysis of mobile phone datasets and networks

127