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Vaclav Belak PhD Viva
 

Vaclav Belak PhD Viva

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  • I’m going to present how to use structure of social interactions to quantify and explain influence between communities.
  • topics flow between communitiescommunities may have a position suggesting an important role as a “bridging” community“Wouldn’t you want to know whether the community you regularly engage with as a researcher is gaining or loosing influence? My research provides answers to such questions.”
  • network represent flow, e.g. frequent information exchangein-degree: frequently responded actor (e.g. cited) is influentialreply as activity stimulationreply as information flowhigh in-degree: control over flow
  • HITS cannot be used to address these questions because it is global measure 1 node vs rest
  • methodological core of our model: COmmunityINfluenceHypothesisof cross-comm impactInfluence measured by impactMembership – distribution of engagement, core vs restCentrality ~ position: control over flow of resource, high/low Cin-degree: tendency to stimulateInfluence ~ stimulation of responses (citations, replies, etc.) by the core members: high/low JDependence ~ community’s activity is driven by core members of other communities
  • independence used to threshold strong impact – community influences activity more than the community itself
  • Boards: 10 yearsSAP: 8 years
  • HITS is a node-level measure and cannot be applied
  • 19 years of data
  • middle period: 1997-2002COLT – strong exporter, Conference on Learning TheoryIJCAI – exports, but consists of core members of other communitiesCBR – isolated, may lead to decline: hard to get external resources like funding or attract new memberswe have much more supportive evidence that CBR declined: size or citation impact
  • Actor-level: Application in public health, marketing, innovation managementCommunity-level: online fora, conferences, any mass-medium; recently gained more attentionSimulation used to simulate the spread
  • Part of the resultsWeek 497, uss=1
  • JELIA - European Conference on Logics in Artificial Intelligence
  • size as a cardinality of the set of the membersdecrease in # papersdecrease in Google Trends since 2005
  • ----- Meeting Notes (17/02/2014 19:16) -----remove or fix
  • 13 strong impacts V-TFL Admin -> V-TFL Discussion

Vaclav Belak PhD Viva Vaclav Belak PhD Viva Presentation Transcript

  • A Structural Approach to Community-level Social Influence Analysis Ph.D. Viva Václav Belák
  • Context and Motivation I Our earlier study suggested communities influence each other 2 / 25
  • Context and Motivation II • Network represents flow between actors • Actor-level social influence in healthcare, innovations, marketing, etc. high in-degree • Actors embedded in communities • No suitable model of community-level influence 3 / 25
  • 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
  • Q1: How can we model influence between communities? 5 / 25
  • Methodology: COIN What impacts depends on How T centrality communities communities membership communities actors actors communities impact 6 / 25
  • Impact and Its Aggregates impacts • • • • • • communities depends on communities Σ Σ 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 7 / 25
  • Experiments 8 / 25
  • 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
  • Personal Issues vs Moderators emphasised: strong impact impacting forum impact 10 ● Personal Issues Moderators 5 PI Mods 0 200 300 400 time  Personal Issues influenced first by Moderators  Later by a specific moderating community, PI Mods 10 / 25
  • Q2: How do we detect communities acting as global authorities/hubs? 11 / 25
  • importance Global Authorities: Widespread High Importance local authorities global authorities low widespread low importance entropy 12 / 25
  • Moderators: Authority of ● importance 2.0 Moderators ● 1.5 ● ● ● ● ● 1.0 ● ● ● ● ● ● ● 0.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ●●● ● ● ● ● ●●● ● ●●● ● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● 0.55 0.60 ● ● ● ● ● ● 0.65 0.70 importance entropy 13 / 25
  • Global Hubs: Widespread High Dependence hubs low widespread low dependence driven dependence entropy 14 / 25
  • After Hours: Hub of dependence ● 10 After Hours 5 ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●● ● ● ●● ● ● ● ● ● ● ●● ●● ●●● ●● ●●● ● ● ●● ● ● ● ●●● ● ● ●● ● ● ●● ● ●● ●● ● ● ● ● ● ●●●●● ●● ●●●●●● ●● ● ● ●●●●● ●●●●●● ●●●●●●●●●●● ●●●●●●●●● ● ● ● ● ● ● ●● ●● ●● ●●●●● ● ●● ● ●● ● ● 0 ● ● 0.4 0.5 0.6 ● ● 0.7 0.8 0.9 dependence entropy 15 / 25
  • Core: Hub of dependence  COIN integrated to SAP PULSAR SAP Business One: Core dependence entropy 16 / 25
  • 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 17 / 25
  • COIN for Scientific Communities • citations as a proxy of impact and information flow citation information flow Aggregate Measures • importance: how much information flows out of the community • independence: how introspective the community is 18 / 25
  • Exporters and Isolated AI Communities Hypothesis • importance indicates exporters • independence and importance indicates isolated islands ● CBR ● ● independence 0.75 islands COLT exporters 0.50 ● 0.25 mainstream ● ● ● 0.00 ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● 0.0 ● ● ● ● ● ● ● loose exporters ● ● ● IJCAI 0.5 1.0 importance 1.5 19 / 25
  • Q3: Can we exploit the model to maximise information diffusion? 20 / 25
  • Influence and Information Diffusion high in-degree Cross-community diffusion maximisation problem: Actor-level diffusion maximisation problem: Which communities to target? Which actors to target? 21 / 25
  • Information Diffusion Experiments • 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. 2. 3. 4. Impact Focus (IF) – COIN Greedy (GR) Group In-degree (GI) Random (RA) IF = importance × entropy • Selection vs Prediction 22 / 25
  • Selection user activation fraction (a) COIN Optimises Information Diffusion 0.05 ● 0.04 ● ● 0.03 0.02 0.01 ● ● 1 user activation fraction (a) Greedy overfits Prediction strategy ● IF GI GR RA 2 3 strategy# seed communities (q) 4 0.05 Impact 5 Focus is more robust ● 0.04 strategy ● IF GI GR RA ● 0.03 ● 0.02 ● 0.01 ● 0.00 1 2 3 4 5 # seed communities (q) 23 / 25
  • 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
  • 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: http://belak.net/doc/2014/thesis.html 25 / 25
  • Personal Issues and Moderators membership indegree 1.00 ● ● 0.75 ld 12 ● 30 ● ● 8 ● 20 0.50 0.25 10 0.00 ● ● ● ● ● ● ● 4 0 PI PIM ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● group PI PIM ● 0 PI PIM PI PIM 26
  • CBR community: isolated CBR in−, out−flow 1.6 1.2 ● ● ● ● out−flow in−flow introspection ● ● out−flow in−flow introspection ● ● 0.8 ● ● ● ● ● ● ● ● ● ● 0.4 ● ● ● ● ● ● 1996 1997 1998 ● ● ● ● 1999 2000 2001 2002 ● ● 2003 2004 2005 2006 2007 2008 year JELIA in−, out−flow 3 ● ● 2 ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 year 27
  • CBR: isolated and shrinking • decreasing size • rigid member-base rising impact factor driven by self-citations ● ● ● ● group in−degree 160 ● ● size ● ● 140 ● ● ● ● ● ● ● ● 120 ● ● ● ● ● ● ● ● ● ● 120 ● ● ● ● ● ● ● ● 80 ● ● ● ● 40 ● ● ● ● ● ● ● ● 1.00 ● ● ● ● impact factor ● ● ● ● ● ● 0.75 ● ● ● ● ● ● 0.50 ● ● ● ● 0.25 ● ● ● ● ● ● 0.00 ● ● ● ● ● ● 1996 1998 2000 2002 2004 2006 2008 1996 1998 2000 2002 2004 2006 2008 1996 1998 2000 2002 2004 2006 2008 year year year  CBR was unable to attract new members and decayed  Cannot be revealed by introspective analysis 28
  • Greedy Strategy 29
  • Group In-Degree GI = # links from outside 30
  • • • • • COIN extended to capture topics Based on tensor algebra Better interpretability and sensitivity Consistent with purely structural COIN actors Topical Dimensions of Influence communities • Example: V-TFL Admin vs V-TFL Discussion 31
  • Rise of Hubs and Authorities in Boards 32
  • Exporters and Introspective Communities 33