1. Structual Trend Analysis for Online Social Networks Ceren Budak Divyakant Agrawal Amr El Abbadi Science,UCSB SantaBarbara,USA Reporter: Qi Liu

2. What to do? Trend traditional structural coordinate 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!

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) 1 N+1 2 2 22 Tx Ty 1 1

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 1 1 1 1 K

9. Significance Validation Model-Based Validation – Independent Trend Formation Model p i,x : external influence q i,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. Tr vs Co vs Un

15. Something new about twitter data Choose 60 th to 100 th 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

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

20. Sampling tech

21. Conclusion Two trend definitons A reduction Sampling tech

22. THE END THANKS!