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MetaFac: Community Discovery via Relational Hypergraph Factorization

MetaFac: Community Discovery via Relational Hypergraph Factorization. Tracking Multiple Relations in Social Media. Yu-Ru Lin 1 , Jimeng Sun 2 , Paul Castro 2 , Ravi Konuru 2 , Hari Sundaram 1 and Aisling Kelliher 1 1 Arts, Media and Engineering, Arizona State University

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MetaFac: Community Discovery via Relational Hypergraph Factorization

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  1. MetaFac: Community Discovery via Relational Hypergraph Factorization Tracking Multiple Relations in Social Media Yu-Ru Lin1, Jimeng Sun2, Paul Castro2, Ravi Konuru2, Hari Sundaram1 and Aisling Kelliher1 1Arts, Media and Engineering, Arizona State University 2IBM T.J. Watson Research Center

  2. The problem

  3. raustin What does s/he like?

  4. How to model multi-relational social data? (Q1) following tweets Favorites History Dugg Comments Favorites Friends raustin

  5. How to model multi-relational social data? How to reveal communities consistent across multi-relations? How to track these communities over time? (Q1) (Q3) (Q2) following tweets Favorites History Dugg Comments Favorites Friends raustin raustin

  6. Our approach

  7. How to model multi-relational social data? (Q1)

  8. Metagraph for modeling multi-relational social data node: facet hyperedge: relation G

  9. How to reveal communities consistent across multi-relations? (Q2) community := a cluster of people who interact with resource and each other in a coherent manner

  10. pc pi|c j xijk cpc∙pi|c∙pj|c∙pk|c k i core tensor Clustering as factorization facet factors

  11. G core tensor facet factors U(1) U(2) U(3) U(4) Factorization on metagraph

  12. Metagraphfactorization (MetaFac) for community extraction on metagraph core tensor data tensor facet factors objective function cost(G)= D((r)||[z] mU(m)) rE m:v(m)~e(r) KL divergence z, {U} can be solved with linear time complexity

  13. How to track these communities over time? (Q3) t-1 t-1 t-1 t-1 t t t t

  14. Metagraphfactorization for Time evolving data (MFT) t-1 objective function cost(G) =  D((r)||[z] mU(m)) cost(G) = (1-) t t t +  {D(zt-1||z)+ D(Ut-1(q)||U(q))} temporal cost

  15. Results

  16. Dataset: Digg 5 facets, 6 relations time span: 3 weeks in Aug 2008

  17. Community analysis C1: gamming industry news C2: US election news C4: general political news C3: world news Change in community size Change in community keywords

  18. Prediction performance Digg prediction Comment prediction

  19. Summary

  20. Problem: How to track communities in dynamic multi-relational data? Approach: MetaFacfor community extraction on metagraph Results: meaningful mining results and best prediction quality

  21. Code / data – available online: http://www.public.asu.edu/~ylin56/kdd09sup.html Questions? Suggestions? Yu-Ru.Lin@asu.edu Thanks!

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