Using Multi-partite Graphs for
Recommendation and Discovery
Michael J. Kurtz, Alberto Accomazzi, andEdwin Henneken
Harvard-Smithsonian Center for
Astrophysics

Networks• 11% Physical Review E• The $200 billion algorithm• 6 degrees of separation• Bacon number  4• Erdos number  3• Gray number  2

Friendship Networks

Moody/Newman

Networks• Objects have properties• Which is the object, which is the property• Do authors have papers• Or do papers have authors• Weighted links Factor analysis,classification mathematics

KWpaper

KWpaper

KWKWpaperpaper

A Bipartite Network

Article Networks

Pavlos Protopapas -Time Series Center• stars• observations• series of observations  time series• similarity measure for TS• groups of similar TS  stars• papers on stars from SIMBAD• KW for papers from ADS• KW  proto-classification of TS/star• Papers• Authors• Readers• Key words• Words• Organizations• References• Date• Data, …

PapersSearch• Different searches from the same query cangive very different results• There is no best answer for all cases

Search• Filter, rank on properties, which
properties, what is the goal?

• User decides (or default)• Example: “weak lensing” 2217 papers inADS, how to rank on properties?

Search• Different goals require different search• User decides on goal=Most citednd2 Order Operators• Use the properties of the results of an
initial query to form a new query• Example: People who bought these books
also bought• In ADS using cites and reads since 1996

1. Get most recent 200 papers containingphrase

2. Find all readers who read one or more ofthese papers within the last three months

3. Find all papers read by these readers
4. Sort by frequency of use

1. Get most relevant 200 papers containingphrase

2. Find all papers cited in the reference
sections of those papers

3. Sort by frequency

1. Get most important (cited) 200 papers
containing phrase

2. Find all papers which cite any of thesepapers

3. Sort by number of papers cited

Browse• User mediated, but not intended to solve
an immediate needUser-Based Browse
1. Create hierarchal system of user clusters
based on shared papers referenced by readpapers
2. For each (sub) cluster get all reads by groupmembers
3. Sort top of most-read list by date (latest tothe top)
4. Iterate

Recommend• No user interaction• What do we know about the user’s current
need?

• Example: user is currently reading an article• Could also use history of user/similar user
behavior, but not yet

Recommend• A complicated process
1. Get normalized key words 991 E.H.
2. Make 991 dim vectors for each paper by KW
frequency in the referenced papers
3. Create user vectors as sum of read papers
4. Create vector space using SVD
5. Fit papers to most significant SVD vectors(50)6. Cluster papers (64) in the ~50 dim SVD vector
space
7. Create an SVD vector sub-space for each clusterof papers
8. Fit papers in each cluster to most significant (5)SVD vectors for relevant subcluster
9. For the paper being read, find the nearest ~40
papers in the relevant sub-space
10. Use these papers to increase the S/N for thedifferent recommender operators

Most after-read, most before-read, most co-read,most recent also-read, most also-read, mostcited, most citing:
For the paper: 2005MNRAS.359..308Z, On theinfluence of relativistic effects on X-ray variabilityof accreting black holes
Most after-read: 2008ApJ...679L..37L, Precise
Measurement of the Spin Parameter of theStellar-Mass Black Hole M33 X-7

Most before-read: 2006ApJ...652..518M, The Spin ofthe Near-Extreme Kerr Black Hole GRS
1915+105
Most co-read: 2006ApJ...646..394M, Simultaneous
Chandra and RXTE Spectroscopy of theMicroquasar H1743-322: Clues to Disk Wind andJet Formation from a Variable Ionized Outflow
Most recent also-read: 2009NewA...14..674F, Whatis the closest black hole to the Sun?

Most also-read: 2009ApJ...695..888U, GRS
1915+105 in "Soft State": Nature of Accretion
Disk Wind and Origin of X-ray Emission
Most cited: 1973A&A....24..337S, Black holes inbinary systems. Observational appearance.
Most citing: 2007ARA&A..45..441M, Relativistic XRay Lines from the Inner Accretion Disks
Around Black HolesA DenselyInterconnected World:
Knowledge Engineering• Semantic interlinking of literature with data
(OAI-ORE, …)• Connecting tools and procedures with eachother and their results (Work-Flow, …))• There is no best bridge, building, … design