A Method of Social Network Extraction via
Internet and Networked Sensing
Takuichi Nishimura, Yutaka Matsuo, Masahiro
Hamasaki, Noriyuki Fujimura, Keisuke Ishida, Tom
Hope, Yoshiyuki Nakamura
Information Technology Research Institute, AIST
1-1-1, Umezono, Tsukuba, Ibaraki 305-8568, Japantaku@ni.aist.go.jp
hamasaki@ni.aist.go.jp
noriyuki@ni.aist.go.jp

Satoshi Fujiyoshi and Kazuya Sakamoto
ALPHA SYSTEMS INC.
Kanagawa, Japanfujiyos@alpha.co.jp

Toru Takahashi
ATR Media Information Science Laboratories
Kyoto, Japan
toru@atr.jp

Hideaki Takeda
National Institute of Informatics
Tokyo, Japan
takeda@nii.ac.jp

Abstract— There are many situations where individuals who
belong to large social networks meet in smaller communities onan ‘ad hoc’ basis. This paper reports on the development and useof a system to support the micro-communities that form in thesesettings. The system combines the benefits of automatic social
network visualization with the ability for members to strengthen
the network using real world encounters via internet andnetworked sensing. Two systems, the web-based Polyphonet
Conference and real-world based Tabletop Community combine toactivate and support user’s wider social network, by engaging
them in multi-media micro-community interactions.
Networked sensing; internet sensing; social network; webmining; real-world oriented interaction; network visualization;social networking service

I.

INTRODUCTION

Interest continues to grow in social networking services(SNS), notably due to the popularity of community web sites,
where users can develop their network by adding known
individuals to it. Perhaps because of this, research emphasis has
focused on the development of communities online, where data
mining can be done to analyze the network of memberships
and relations. Increasingly, data is being produced that showsthe ‘small-world’ nature of online communities [1] and theeffect of online communities in the real world [2].

This work has been partially supported by New Energy and Industrial
Technology Development Organization (NEDO) as the project ID of
04A11502a.

Research into human interaction in organizations suggeststhat awareness of the activities of other members of thecommunity is an important factor in who will receive or giveinformation [3]. More recently in the ubiquitous computing
community, the importance of embodied interaction [4] hasbegun to be recognized in tangible computing [5].
Though many types of communities exist online, we focus
on the development of a system utilizing networked sensors to
support real-world community, incorporating insights fromboth studies of online community and real-world community
interaction. It is clear that people have multiple memberships ofdifferent social networks, yet there are occasions where those
networks can be expanded and strengthened significantly by
real-world interaction. The system we present uses this realworld environment to help users engage with previously known
and newly met individuals in contexts of viewing their(visualized) social networks. This combination of web-basedsocial network analysis, an online interactive SNS with richcontents, and an integrated on-site system of SN modification
and representation is a new development in bridging the onlineoffline divide via internet and networked sensing.
Focusing on real-world community and especiallycommunities that have pre-existent interest in each other’s
associations and relationships, we have a long-term goal:
strengthening the wider social network of the users. This isdone by introducing a tangible interface in temporary situationsof ‘micro-community’ interaction, namely, the meeting of users
around a table with multimedia sensors. The aspect of thesystem – the Tabletop Community – provides users with theability to form ‘communities of memory’ [6] as multimedia

contextual information is ‘tagged’ to the edges between nodesof their social networks. This information, which is shared only
between those who physically interact with each other at theconference, builds a strong sense of community, which
subsequently activates the wider social network of users at theconference and after the event.
The next section presents a description of the basic systemdesign of the social network visualization for community
support. In section III and section IV, we propose the
Polyphonet conference, which obtains social networks
automatically from the web and which can improve from userinteraction to the system and Tabletop Community, which
capture the action of micro-communities. We demonstrate theeffectiveness of our system in section V. We conclude thispresentation and show future work in section VI.
II.

HYBRID SOCIAL NETWORK EXTRACTION

It is important for participants to know what kinds ofpeople participate and which participants share similar
concerns to activate discussion at academic conferences andpromote communication among participants. A participant listthat has participants’ names, affiliations, etc. and position
papers are good information for showing what kinds of people
participate. That information is personally attributive
information. On the other hand, relationships among users in
the community are numerous and sometimes quite strong. Suchsocial networks should be analyzed and revealed to users. Suchuser feedback has rarely been attempted in the field ofsociology.
There are various methods to analyze and obtain the social
network in a community. Conventionally, questionnaires andinterviews were main method for such network analyses.
Recently, communication by e-mail [12], Multimedia BBS [15]and links among web pages [11] have served similar purposes.
Semantic map [7] has a similar purpose to our system. It
shows networks among participants and exhibits, and keywords.
Participants register interesting exhibits to the system with a
PDA. Then the system makes relationships among participantsthat registered the same exhibits. Our previous system [8]shows relationships among persons via their Web bookmarks.
Referral Web [9] and [10] extract social networks from the web
automatically. Our previous system [10] attempts to discover
the human relational network using the number of retrieved
documents.
Table I shows categories of methods to extract social
networks. ‘Precision’ means the quality of extracted humanrelationships if each method is used to extract social network.
In the column of extraction ‘Cost’, we compared the cost to
extract SN with same numbers of members.

TABLE I.

Interview,questionnaire
Mail, BBS
Links among
home page
Semantic map,
kMediaReferral Web,
Matsuo[10]TABLE II.

Polyphonet
Polyphonet
Tabletop
Community

METHODS OF EXTRACTING SOCIAL NETWORKS
Information
SourceSourceType
PersonDescription

Precision

Cost

High

High

Mail, BBS
Web

High
MiddleMiddleLow

MiddleMiddleLow

Low

Interaction
Interaction

Favorite List, Profile
Bookmark
ProfileWeb

METHODS TO EXTRACT SOCIAL NETWORKS OF OUR SYSTEM
Information
SourceWeb
Personknow link
PersonTouch link

SourceType
ProfileDescription
Interaction

Precision

Cost

Middle
High

Low
Low

High withmultimedia
data

MiddleNotwithstanding, conventional methods cannot retrievesocial networks among a community automatically and further
improve them using user-system interactions. We propose a
combined social network extraction system that includes many
web services based on community interests and contents andonsite system that have been deployed in real-world space forsupporting mobile users in the site.
We think it is important to create the initial network
without demanding any personal information input except
name and affiliation from the user to make the whole social
network useful. Next, the initial network should be modified
according to the user interaction with the system. The web
system that has a click button to show that you know thisperson or you are interested in some content, which might
resemble the preferences of other users. Such information fromweb systems is added to the social network. Thirdly, onsite
interaction information is also added. For example, if threeusers used the same table together and the same demonstration
was visited simultaneously by two other users. Such a small
group might be called a micro-community or temporarycommunity; we believe that such small and short-lived
communities will bolster the usefulness of social networks.
In section 3 and section 4, we realize one example of social
network visualization for community support by seamlessly
combining two systems, Polyphonet Conference and Tabletop
Community, which are applied to a ubiquitous international
conference, Ubicomp2005. The Polyphonet Conference (wecall this system Polyphonet hereafter) creates an initial networkfrom the web and gathers user clicks of the acquaintance button
as shown in Table II. Tabletop Community sends the IDs oftouched cards which exist simultaneously on the table to the
Polyphonet. The Tabletop Community is designed to attract
users by providing micro-community scene capturing andbrowsing services as a personal image network.

III.

POLYPHONET CONFERENCE

Polyphonet is a social network browser and a conference
scheduling system. Polyphonet obtains attendees' relationships
and research topics from web sites. A user can find what
research topic a researcher is doing or whom she is working
with. In the scheduling part, a user can register interestingpresentations (papers, demos and posters) and get
recommended presentations and other researchers.
Polyphonet has three methods to extract social networks
among participants. The first one is based on web mining
techniques. The second one and third one are based on userinteraction on the web system and at the conference.
A. Edges Extracted from the Web
We applied the web mining method based on method [10]to extract a social network among participants. In this section,
we give a synopsis of the method and modifications for oursystem.
The simplest approach is to measure the relevance of two
nodes based on the number of retrieved results obtained by asearch engine query. For example, assume we are to measure
the relevance of two names “Mark Weiser” (denoted X) and“Alan Key” (denoted Y). We first address a query “X and Y”
to a search engine and get a documents including those words
in the text. In addition, we make two queries “X” and “Y”, andget b1 and b2 documents. The relevance of “Mark Weiser” and“Alan Key” is approximated by the Simpson coefficient.

rel ( x, y )= Simpson( X , Y ) =#(X∩Y )min(# ( X ), # (Y ))Say a is divided by b. The rel(x,y) represents the relevance ofnode x and y.
B. Edges Registered by Users
Creation of personal acquaintance lists ("I-know" and "I'mknown-by" lists) is a basic function. A user can make an
addition to the "I-know" list when that user finds anacquaintance on paper or session pages or authors and otherusers listed in order of calculated weight. At that time, theacquaintance is also added to the acquaintance’s "I'm-knownby" list. These actions are additions of a know-edge.
IV.

TABLETOP COMMUNITY

A. System Design
We developed a system based on the idea of a microcommunity similar to that created around tables and situations
where people are drinking coffee in a casual way.
Using a tabletop/desktop as horizontal interactive humancomputer interface has been examined by many researchers[13]. However, the cultural context that a table has is the mainconcern that the system focuses on. Tables in cafés, kitchen
tables, and/or dining tables have been playing an important role
to form communities for a long time in human history. The
table is used here as community device. Computer technology

is used in this system to enhance and extend this culturalcontext of the table in order to form micro-communities [14].
Therefore, the environment that the system is targeting issuch as a corner of conference room and places for coffee
breaks. In contrast to environments such as that in the middle
of a big conference room, a much closer relationship in a
micro-community will occur in this kind of environment andoccasion.
We also address the temporary-community that is also
being created around a similar situation. The system also aims
to maintain a temporary-community to a long term one in case
it is needed. We developed the system based on the following
three conditions to realize the vision we propose above.
1)
The system has a function of sensing the state of the
micro-community by using visual sensors, audio sensors, ID
sensors, vibration sensors, temperature sensors, etc.
2) The system has a function of reviewing past actions of
the micro-community.
3) The system has Monitor displays for displaying social
network as well as items of common interest amongcommunity members, such as schedules, private informationand social networks.
To include the conditions above, Tabletop Community has
the following function aside from those of Polyphonet. First,
the system can capture information such as visual informationof the people around the table and additional information suchas audio/voice data and ID data. The capture will occur when
the user desires it. A typical instance is when the user applies auser device to the system.
Secondly, to allow community members to shareexperiences with other members, the system has a function ofvisualizing the human network inside of the micro-community
with rich multimedia data.
We also use user device ID information that the Tabletop
Community obtains to improve the precision of the data ofsocial network of the entire community that Polyphonet targets.
In the next section, we will describe one example of such
Tabletop Communities which we have developed.
B. Implementation
We have developed the system shown as Fig. 1. We chose
IC cards as a user device because they are gaining popularity
and are becoming widely used in Japan. An omni-directional
camera and IC card readers are on the table of the system.
Using these camera and card readers, when the user puts an IC
card onto a reader, the system detects that individual’s roughlocation (direction of the user from the camera) andidentification of users in the picture, which is taken by anomni-directional camera every time when a user
places/withdraws a personal IC card.

Figure 1. Tabletop community usage.

Figure 3. Snapshots are shown as a network from users.

Figure 4. Pictures from the installation of Tabletop Community
Figure 2. Social networks among three users

Each picture has additional information in addition tographical data such as the name of the photo, the time and datewhen the photograph was taken, a unique ID number of thetable (if we use more than one table): all the ID information of
the IC cards from card readers. These data will be stored ineach PC as well as the PC that is connected directly to thecamera and card readers via a network. We infer that it ispossible to put a database server in the middle of the networkof the Tabletop Community system and store all photographdata in it. However, considering the bandwidth and stability of
the network we use, we chose to use a decentralized databasesystem instead.
When a single participant puts an IC card on a card reader,that participant can log in directly to the Polyphonet
Conference. If two or three participants put IC cards together,
they can see social networks among them (Fig. 2). Then thesocial-tie “We meet and see social networks together” is added
automatically to the Polyphonet conference. These actions in
the real word mean additions of know-edges.

Here we show another monitor screen screenshot (Fig. 3). It
illustrates three IC card readers on the table for three users.
Unlike the static screenshot implies this network of pictures
reacts to a change of the network as well as users’ interaction
through a mouse pointer as if it were living. A picture that has
all three users moves to the center of the screen. A picture
showing only one of three users moves towards the edges ofthe screen. This motion is created by calculation based on basic
physical simulation. We applied two physical simulation
models onto the screen. One is repulsion among each picture.
The other one shows that attraction between the user node andthe photograph node relates to the user using a spring model.
This simple simulated physics in the visualization programautomatically lays out the pictures and its network on thescreen in an artistic way.
V.

FIELD TESTING

We tested our system at a three-day international
conference. In this conference, there were 122 presentations
and 355 authors (including co-authors). About 500 participantsjoined the conference. We opened Polyphonet conference aweek before the conference and deployed four tabletopcommunities on site. Fig. 4 shows the hardware components ofthe tabletop community in the conference room.

450
400
350

A. Results
The number of the Polyphonet users is 308 and 175 users
registered from the web system and 133 users are invited frominformation kiosks in the conference room.

300
250

150

Polyphonet extracts social networks among participants
with three methods. First, the system extracts them using Webmining technique as initial data (Mining-link). Second, users
can register their own social network with our system (Knowlink). Third, two or three users can register their meetingthrough the information kiosk (Touch-link).

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Figure 5. The number of Touch-links and Know-links

The number of Web-Mining link, Know link and Cardtouch link is 22,332, 924 and 726, respectively. The number of
Mining-links is much larger than the others. This tendency isgood because our system use Mining-links as an initial data ofsocial networks. It is desirable that every user should have theirown initial data.
Fig. 5 shows the number of Touch-links and Know-links.
Users check Know-link on Polyphonet and touch the user
device on a card reader of Tabletop Community. The numberof Know-link is almost constant and the number of Touch-link
is high on the first two days. This results show both link data
are important and Touch-link is more convenient during theconference period.
Fig. 6 shows the networks extracted from Know-links,
Touch-links and web mining-links, from left to right of the
figure, respectively. In this figure, the threshold of the mininglink network is controlled in order to reduce the number ofedges for clear visualization.
VI.

CONCLUSION

In this paper, we described the system design of a methodof hybrid social network extraction. We integrated the
Polyphonet conference in order to obtain an initial network andTabletop Community, which captures the action of microcommunities with vision and IC card sensors. The field testshows the system has potential for gaining social network
utilizing both internet and networked sensing. Future work
includes improving the network visualization method
especially how to clearly visualize various kinds of edges andshows the accuracy of the network.
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