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Cascading Behavior in Large Blog Graphs Patterns and a Model

Cascading Behavior in Large Blog Graphs Patterns and a Model. Leskovec et al. (SDM 2007). Why?. Temporal Aspects How does information spread in Social Network? How does the popularity die? Linearly, exponentially, or …? Topological Aspects Do information cascades have common structures?

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Cascading Behavior in Large Blog Graphs Patterns and a Model

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  1. Cascading Behavior in Large Blog GraphsPatterns and a Model Leskovec et al. (SDM 2007)

  2. Why? • Temporal Aspects • How does information spread in Social Network? • How does the popularity die? Linearly, exponentially, or …? • Topological Aspects • Do information cascades have common structures? • Their properties like size distribution

  3. Preliminaries • Trivial vs. Non-trivial Cascades • Cascade Initiator • Stars and Chains • Connector nodes

  4. Dataset • 21.3 million posts, 2.5 million blogs from Aug and Sep 2005 • Start with most cited blog posts in Aug’05 • Traversed conversations forward (inlinks) and backward (outlinks) • Max depth = 100; max breadth = 500 • Collected • Unique post ID • Blog URL • Post Permalink • Post Date • Post Content • Post Links

  5. Temporal Patterns How Popularity dies?

  6. Blog Network Topology Popular blogs that receive lots of inlinks does not necessarily sprout many outlinks.

  7. Post Network Topology 98% of the posts are isolated

  8. Topological Patterns Common Cascade Shapes (Gr has the frequency rank r) 97% are trivial cascades

  9. Topological Patterns Cascade Size Distribution

  10. Observations • Most cascades follow tree like structures. • Linear increase in diameter requires exponential increase in the cascade size. • The probability that a node will be a part of a cascade decreases with the number of cascades it is already a part of.

  11. Generative Model • Susceptible-Infected-Susceptible (SIS) Model • β: “infection probability” of a post • Blog can be either “infected” or “susceptible”

  12. Summary • Temporal patterns • Topological patterns • Generative model

  13. Food for thought • Blogs are sparsely linked. Not many posts link to the original post from which they got the content. How to study information diffusion in these scenarios? • Beyond link analysis • Uniform infecting probability is an unrealistic assumption • Multiple cascades initiating simultaneously • Not many study the “tipping point” in cascades • Does the cascade die its natural death or is there some factor that affects the lifespan of a cascade

  14. Inlink Outlink T-1 T T+1 Backward Forward

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