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A Structural Approach to Community-level Social Influence Analysis

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  1. A Structural Approach to Community-level Social Influence Analysis Ph.D. Viva Václav Belák

  2. Context and Motivation I Our earlier study suggested communities influence each other

  3. Context and Motivation II high in-degree • Network represents flow between actors • Actor-level social influence in healthcare, innovations, marketing, etc. • Actors embedded in communities • No suitable model of community-level influence

  4. Research Problem and Questions Questions How can we model influence between communities? How do we detect communities acting as global authorities/hubs? Can we exploit the model to maximiseinformation diffusion? Problem: measurement, analysis, and explanation of influence between various types of social communities

  5. Q1: How can we model influence between communities?


  6. Methodology: COIN What impacts depends on communities communities communities How centrality impact membership communities actors actors T

  7. Impact and Its Aggregates impacts Σ Σ depends on communities • row – impact of a community on others • column – impact of others on a community • diagonal – independence • importance = total impact of a community on others • dependence = total impact of others on a community • importance/dependence heterogeneity measured by entropy communities

  8. Experiments

  9. Influence Over Time • Questions: • Which communities influenced a given community over time? • How do we measure that by COIN? • Hypothesis • Frequent impact higher than independence indicates influence • Experiments • segment data by time window • find impact higher than independence of influenced community • Discussion fora data • links represent replies • forum as a proxy of community

  10. Personal Issues vs Moderators emphasised: strong impact • Personal Issues influenced first by Moderators • Later by a specific moderating community, PI Mods

  11. Q2: How do we detectcommunities acting as global authorities/hubs?

  12. Global Authorities: Widespread High Importance local authorities global authorities importance widespread low low importance entropy

  13. Moderators: Authority of Moderators importance importance entropy

  14. Global Hubs: Widespread High Dependence driven hubs dependence low widespread low dependence entropy

  15. After Hours: Hub of After Hours dependence dependence entropy

  16. Core: Hub of • COIN integrated to SAP PULSAR SAP Business One: Core dependence dependence entropy

  17. Cross-Community Dynamics in Science • Questions • How can we measure and explain influence between scientific communities? • How does the influence relate to community’s performance? • How do we adapt COIN? • Data • Scientists linked by citations • AI communities defined as conferences

  18. COIN for Scientific Communities citation information flow • citations as a proxy of impact and information flow • Aggregate Measures • importance: how much information flows out of the community • independence: how introspective the community is

  19. Exporters and Isolated AI Communities COLT IJCAI CBR islands exporters independence loose exporters mainstream importance • Hypothesis • importance indicates exporters • independence and importance indicates isolated islands

  20. Q3: Can we exploit the model to maximiseinformation diffusion?

  21. Influence and Information Diffusion high in-degree Actor-level diffusion maximisation problem: Which actors to target? Cross-community diffusion maximisation problem: Which communities to target?

  22. Information Diffusion Experiments IF = importance × entropy Impact Focus (IF) – COIN Greedy (GR) Group In-degree (GI) Random (RA) • Selection vsPrediction • Hypothesis: product of importance and entropy identifies seed communities that induce high overall adoption • Overall adoption estimated by a diffusion model on • Four targeting strategies:

  23. COINOptimises Information Diffusion Selection Greedy strategy overfits Impact Focus is more robust Prediction

  24. Summary and Future Work • COIN: computational model for community influence • Communities influencing a particular community • Roles of communities: authorities vs hubs • Isolated communities loosing influence • Seed communities for information diffusion • General (3 systems) and extensible • Tensor-based extension of COIN captures topics • Future Work • May be applicable to e.g. email networks • Impact Focus may be improved by discounting overlap • Sentiment-informed community influence

  25. Contributions http://belak.net/doc/2014/thesis.html • proposes a solution to the problem of measurement, analysis, and explanation of influence between communities • purely structural approach • extended to capture topics • empirical analysis of 3 systems – common/different phenomena • first approach to novel problem of cross-community information diffusion • Dissemination • 1 journal, 3 conference, and 1 workshop papers • best poster at NUIG research day 2013 • complete results, software, data, thesis, etc. at:

  26. Personal Issues and Moderators

  27. CBR community: isolated CBR JELIA

  28. CBR: isolated and shrinking • decreasing size • rigid member-base rising impact factor driven by self-citations • CBR was unable to attract new members and decayed • Cannot be revealed by introspective analysis

  29. Greedy Strategy

  30. Group In-Degree GI = # links from outside

  31. Topical Dimensions of Influence actors topics communities • COIN extended to capture topics • Based on tensor algebra • Better interpretability and sensitivity • Consistent with purely structural COIN • Example: • V-TFL Admin vs V-TFL Discussion

  32. Rise of Hubs and Authorities in Boards

  33. Exporters and Introspective Communities