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Structual Trend Analysis for Online Social Networks

Structual Trend Analysis for Online Social Networks. Ceren Budak Divyakant Agrawal Amr El Abbadi Science,UCSB SantaBarbara,USA Reporter: Qi Liu. What to do?. traditional. Trend. coordinate. structural. uncoordinate. What’s new?. Structural trend definition

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Structual Trend Analysis for Online Social Networks

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  1. Structual Trend Analysis for Online Social Networks CerenBudakDivyakantAgrawalAmr El Abbadi Science,UCSBSantaBarbara,USA Reporter: Qi Liu

  2. What to do? traditional Trend coordinate structural uncoordinate

  3. What’s new? • Structural trend definition • Reducing to local triangles counting • Sampling tech for online detection

  4. From where? • A temporal view • Using spatial properties • Counting, streaming and semi-streaming

  5. Define it! • Directed G=(N,E) • ejiϵE =>ni is one neighbor of nj • ni mentions Tx=> <ni, Tx> Traditional: Coordinate: Uncoordinate:

  6. High scores for coordinated trend • Large number of pairs of connected nodes • Large number of mentions • For a complete graph, favors a uniform distribution • In a power law graph, biased toward influential nodes

  7. Example for complete graph f(Tx) = f(Ty) = 2N g(Tx) = 3N(N-1) g(Ty) = 4N(N-1) N+1 Ty Tx 2 2 1 1 1 2 2

  8. Example for power law graph f(Tx) = f(Ty) = K+N-1 g(Tx) = 2K(N-1) g(Ty) = 2K+2N-4 Tx Ty K 1 1 K 1 1 1 1

  9. Significance Validation • Model-Based Validation • Independent Trend Formation Model • pi,x: external influence • qi,j,x: internal influence • Nearest Neighbor model • u: probability from 2 to 1 • k: pairs of connected nodes per step • Analysis-Based Validation

  10. Coordinated differs from traditional • Spearman rank correlation coefficient(SRCC) • [-1, +1] • Average precision • difference

  11. What topics detected? • Vary p and q • Using different score functions • Results:

  12. App: Sybil Attack Detection • Ranking of Ty: co>tr>un • Breakpoints may means attack • Small p,q and few Sybil nodes, big effect

  13. Analysis-Based Validation • Twitter data: 467 million posts, 20 million users, spanning 7 months • 230m posts, 2.7m users, 2960495 hashtags Extraction

  14. Trvs Co vs Un

  15. Something new about twitter data • Choose 60th to 100th topics • Findings: • coordinated trend: 7694 users, 21.5 edges on average; • uncoordinated trend: 21114 users, 8.6 edges

  16. Prefuse

  17. Hashtag categories effect • 7 categories: political, technology, celebrity, games, idioms, movies, music and none

  18. Incremental Counting Algorithm • For a coming <nl,Tx>

  19. Reducing to count local triangles • A directed multi-graph G’ = (N’,E’) • N’ = T U N, E’ = Et U Ef

  20. Sampling tech • How to work? • Correctness: • Xx = Countx/ (ps)^2, : triangles sharing edges

  21. Conclusion • Two trend definitons • A reduction • Sampling tech

  22. The ENDTHANKS!

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