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Models of Communication Dynamics for Simulation of Information Diffusion

Models of Communication Dynamics for Simulation of Information Diffusion. Malik Magdon-Ismail , Konstantin Mertsalov, Mark Goldberg. Motivation. Important to understand information diffusion in social networks Viral marketing, gossip, rumors, etc. Social Networks are dynamic

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Models of Communication Dynamics for Simulation of Information Diffusion

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  1. Models of Communication Dynamics for Simulation of Information Diffusion Malik Magdon-Ismail, Konstantin Mertsalov, Mark Goldberg

  2. Motivation • Important to understand information diffusion in social networks • Viral marketing, gossip, rumors, etc. • Social Networks are dynamic • Edges and nodes change with time. • Cannot repeat historical dynamics, so need to simulate dynamics for research.

  3. LiveJournal Data Alice’s Blog Bill’s Blog Alice Posted Bill Posted A • Bill commented • Alice commented • Cory • commented Edges: (A,B); (C,B) (D,B) • Alice commented • Cory commented • Dave commented Edges: (B,A); (C,A) D B C Construct sequence of comment graphs (every week)

  4. Dynamics of LiveJournal Network • 60 weeks • Per week: • 153,028 nodes • 510,317 edges • Very dynamic: • 70% of edges change from week to week

  5. Diffusion in Dynamic Networks Time: T Time: T+1 Time: T+2 C C C C C A A A A A B B B B B D D D D D F F F F F E E E E J J J J E J Static H H H H H G G G G G C A B D Dynamic F E J H G

  6. Diffusion in LiveJournal Blogs Linear Threshold Independent Cascade Diffusion model and network dynamics have a big impact on infection.

  7. Goal Can we model the network dynamics so that diffusion in the model mimics diffusion in the real network?

  8. Modeling Dynamics Output Network at iteration t+1 Input Network at iteration t C C A A B D B D E J E J F F H H G G

  9. A General Model Input: Gt Step 1: Find Locality C C A A B D B D F E J F E J H H G G Step 2:Local Attachment Output: Gt+1 C A C B D A B D F E J F E J H G H G

  10. Ingredients to General Model 1. What is the locality of the node ? C A • Global: all nodes • k-Neighborhood • Community* B D Gt F E J H G 2. How to attach within locality ? C A B D • Uniform • Preferential Attachment • Random walk Gt+1 F E J H G * Community = union of overlapping clusters [Baumes, Goldberg, Magdon-Ismail 2005]

  11. Diffusion Models C C Linear Threshold: • Node i has a susceptibility fraction T(i). • Node i infected if at least T(i) neighbors are infected. A A B B D D F F E E J J H H G G C C A A Independent Cascade: • Every edge (i,j) has transmission prob. P(i,j). • Nodes have one chance to infect neighbors. B B D D 0.3 0.2 F F E E J J 0.4 H H G G

  12. Diffusion in Dynamic Network Diffusion Model Network Dynamics Real LiveJournal Cascade Diffusion Progression Locality and Attachment Threshold Model

  13. Results Dynamic Network Static Aggregated Network Cascade Threshold

  14. Conclusions • Dynamics of the network strongly affects it’s diffusion properties • Global random link dynamics does not model dynamics correctly • Social network links evolve through locality (social groups), eg. cluster-based communities+PA produces diffusion faithful network dynamics.

  15. Thank you !

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