1 / 22

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

maxime
Télécharger la présentation

Structual Trend Analysis for Online Social Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!

More Related