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A Structural Approach to Community-level Social Influence Analysis Ph.D. Viva Vclav Belk

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Context and Motivation I Our earlier study suggested communities influence each other 2 / 25

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Context and Motivation II 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 high in-degree 3 / 25

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Research Problem and Questions Problem: measurement, analysis, and explanation of influence between various types of social communities Questions 1.How can we model influence between communities? 2.How do we detect communities acting as global authorities/hubs? 1.Can we exploit the model to maximise information diffusion? 4 / 25

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Q1: How can we model influence between communities? 5 / 25

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Methodology: COIN How What centrality actors communities impact communities membership actors communities T 6 / 25 impacts depends on

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Impact and Its Aggregates 7 / 25 impacts 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

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Experiments 8 / 25

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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 9 / 25

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Personal Issues vs Moderators Personal Issues influenced first by Moderators Later by a specific moderating community, PI Mods emphasised: strong impact 10 / 25

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Q2: How do we detect communities acting as global authorities/hubs? 11 / 25

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12 / 25 importance importance entropy global authorities local authorities low widespread low Global Authorities: Widespread High Importance

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13 / 25 importance importance entropy Moderators Moderators: Authority of

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14 / 25 dependence dependence entropy hubsdriven lowwidespread low Global Hubs: Widespread High Dependence

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15 / 25 dependence dependence entropy After Hours: Hub of After Hours

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16 / 25 SAP Business One: Core Core: Hub of dependence entropy dependence COIN integrated to SAP PULSAR

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Cross-Community Dynamics in Science Questions How can we measure and explain influence between scientific communities? How does the influence relate to communitys performance? How do we adapt COIN? Data Scientists linked by citations AI communities defined as conferences 17 / 25

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COIN for Scientific Communities 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 18 / 25 citation information flow

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Exporters and Isolated AI Communities Hypothesis importance indicates exporters independence and importance indicates isolated islands 19 / 25 independence importance exportersislands mainstream loose exporters CBR COLT IJCAI

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Q3: Can we exploit the model to maximise information diffusion? 20 / 25

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Influence and Information Diffusion Actor-level diffusion maximisation problem: Which actors to target? Cross-community diffusion maximisation problem: Which communities to target? high in-degree 21 / 25

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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: 1.Impact Focus (IF) COIN 2.Greedy (GR) 3.Group In-degree (GI) 4.Random (RA) Information Diffusion Experiments IF = importance entropy 22 / 25 Selection vs Prediction

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COIN Optimises Information Diffusion Selection Prediction Greedy strategy overfits Impact Focus is more robust 23 / 25

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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 24 / 25

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Contributions 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: 25 / 25 http://belak.net/doc/2014/thesis.html

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Personal Issues and Moderators 26

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CBR community: isolated CBR JELIA 27

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

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Greedy Strategy 29

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Group In-Degree 30 GI = # links from outside

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Topical Dimensions of Influence 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 actors communities topics 31

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Rise of Hubs and Authorities in Boards 32

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Exporters and Introspective Communities 33