Structual Trend Analysis for Online Social Networks
Ceren Budak Divyakant Agrawal Amr El AbbadiScience,UCSB SantaBarbara,USA
Reporter: Qi Liu
What to do?
Trend
traditional
structural
coordinate
uncoordinate
What’s new?
• Structural trend definition• Reducing to local triangles counting• Sampling tech for online detection
From where?
• A temporal view• Using spatial properties• Counting, streaming and semi-streaming
Define it!
• Directed G=(N,E)• ejiϵE => ni is one neighbor of nj
• ni mentions Tx => <ni, Tx>
Traditional:Coordinate: Uncoordinate:
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
Example for complete graph
f(Tx) = f(Ty) = 2Ng(Tx) = 3N(N-1) g(Ty) = 4N(N-1)
1
N+1
2
2
2 2
Tx Ty
1
1
Example for power law graph
f(Tx) = f(Ty) = K+N-1g(Tx) = 2K(N-1) g(Ty) = 2K+2N-4
Tx
Ty
K
111
1
11K
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
Coordinated differs from traditional
• Spearman rank correlation coefficient(SRCC)– – [-1, +1]
• Average precision–
• difference
What topics detected?
• Vary p and q• Using different score functions• Results:
App: Sybil Attack Detection
• Ranking of Ty: co>tr>un• Breakpoints may means attack• Small p,q and few Sybil nodes, big effect
Analysis-Based Validation
• Twitter data: 467 million posts, 20 million users, spanning 7 months
• 230m posts, 2.7m users, 2960495 hashtags
Extraction
Tr vs Co vs Un
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
Prefuse
Hashtag categories effect
• 7 categories: political, technology, celebrity, games, idioms, movies, music and none
Incremental Counting Algorithm
• For a coming <nl,Tx>
•
Reducing to count local triangles
• A directed multi-graph G’ = (N’,E’)• N’ = T U N, E’ = Et U Ef
Sampling tech
• How to work?• Correctness:–
– Xx = Countx / (ps)^2, : triangles sharing edges
Conclusion
• Two trend definitons• A reduction• Sampling tech
THE ENDTHANKS!
