1. Neighborhood Formation and Anomaly Detection in Bipartite Graphs Jimeng Sun Huiming Qu Deepayan Chakrabarti Christos Faloutsos Speaker: Jimeng Sun

2. 2 Bipartite Graphs G={V 1 +V 2, E} such that edges are between V 1 and V 2 Many applications can be modeled using bipartite graphs The key is to utilize these links across two natural groups for data mining

3. 3 Problem Definition Neighborhood formation (NF) Given a query node a in V 1, what are the relevance scores of all the nodes in V 1 to a ? Anomaly detection (AD) Given a query node a in V 1, what are the normality scores for nodes in V 2 that link to a ? V1V2 a.3.2.05.01.002.01.25.05

4. 4 Application I: Publication network Authors vs. papers in research communities Interesting queries: Which authors are most related to Dr. Carman? Which is the most unusual paper written by Dr. Carman?

5. 5 Application II: P2P network Users vs. files in P2P systems Interesting queries: Find the users with similar preferences to me Locate files that are downloaded by users with very different preferences users files

6. 6 Application III: Financial Trading Traders vs. stocks in stock markets Interesting queries: Which are the most similar stocks to company A? Find most unusual traders (i.e., cross sectors)

7. 7 Application IV: Collaborative filtering collaborative filtering recommendation system CustomersProducts

8. 8 Outline Problem Definition Motivation Neighborhood formation Anomaly detection Experiments Related work Conclusion and future work

9. 9 Outline Problem Definition Motivation Neighborhood formation Anomaly detection Experiments Related work Conclusion and future work

10. 10 Neighborhood formation – intuition Input: a graph G and a query node q Output: relevance scores to q random-walk with restart from q in V 1 record the probability visiting each node in V 1 the nodes with higher probability are the neighbors V1V2 q.3.2.05.01.002.01

11. 11 Exact neighborhood formation Input: a graph G and a query node q Output: relevance scores to q Construct the transition matrix P where every node in the graph becomes a state every state has a restart probability c to jump back to the query node q. transition probability Find the steady-state probability u which is the relevance score of all the nodes to q q c cc c (1-c) c

12. 12 Approximate neighborhood formation Scalability problem with exact neighborhood formation: too expensive to do for every single node in V 1 Observation: Nodes that are far away from q have almost 0 relevance scores. Idea: Partition the graphs and apply neighborhood formation for the partition containing q.

13. 13 Outline Problem Definition Motivation Neighborhood formation Anomaly detection Experiments Related work Conclusion and future work

14. 14 Anomaly detection - intuition t in V 2 is normal if all a in V 1 that link to t belong to the same neighborhood e.g. low normalityhigh normality t t

15. 15 S Anomaly detection - method Input: a query node q from V 2 Output: the normality score of q Find the set of nodes connected to q, say S Compute relevance scores of elements in S, denoted as rs Apply score function f(rs) to obtain normality scores: e.g. f(rs) = mean(rs) q

16. 16 Outline Problem Definition Motivation Neighborhood formation Anomaly detection Experiments Related work Conclusion and future work

17. 17 Datasets datasets|V 1 ||V 2 ||E| Avgdeg(V 1 )Avgdeg(V 2 ) Conference- Author (CA) 2687288K662K5105 Author- Paper (AP) 316K472K1M32 IMDB553K204k2.2M411

18. 18 Goals [Q1]: Do the neighborhoods make sense? (NF) [Q2]: How accurate is the approximate NF? [Q3]: Do the anomalies make sense? (AD) [Q4]: What about the computational cost?

19. 19 [Q1] Exact NF The nodes (x-axis) with the highest relevance scores (y-axis) are indeed very relevant to the query node. The relevance scores can quantify how close/related the node is to the query node. relevance score most relevant neighbors relevance score most relevant neighbors ICDM (CA) Robert DeNiro (IMDB)

20. 20 [Q2] Approximate NF Precision = fraction of overlaps between ApprNF and NF among top k neighbors The precision drops slowly while increasing the number of partition The precision remain high for a wide range of neighborhood size neighborhood size = 20 num of partitions = 10 # of partitions Precision neighborhood size

21. 21 [Q3] Anomaly detection Randomly inject some nodes and edges (biased towards high-degree nodes) The genuine ones on average have high normality score than the injected ones normality score

22. 22 [Q4] Computational cost Even with a small number of partitions, the computational cost can be reduced dramatically. Approximate NF Time(sec) # of Partitions

23. 23 Related Work Random walk [Brin & Page98] [Haveliwala WWW02] Graph partitioning [Karypis and Kumar98] [Kannan et al. FOCS00] Collaborative filtering [Shardanand&Maes95] … Anomaly detection [Aggarwal&Yu. SIMOD01] [Noble&Cook KDD03] [Newman03]

24. 24 Conclusion Two important queries on bipartite graphs: NF and AD An efficient method for NF using random- walk with restart and graph partitioning techniques Based the result of NF, we can also spot anomalies (AD) Effectiveness is confirmed on real datasets

25. 25 Future work and Q & A Future work What about time-evolving graphs? Contact: Jimeng Sun jimeng@cs.cmu.edu http://www.cs.cmu.edu/~jimengwww.cs.cmu.edu/~jimeng