1. A Structural Approach to Community-level Social Influence
Analysis Ph.D. Viva Vclav Belk
2. Context and Motivation I Our earlier study suggested
communities influence each other 2 / 25
3. 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
4. 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
5. Q1: How can we model influence between communities? 5 /
25
6. Methodology: COIN What impacts depends on How T centrality
communities communities membership communities actors actors
communities impact 6 / 25
7. Impact and Its Aggregates impacts communities 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 7 / 25
8. Experiments 8 / 25
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 9 /
25
10. 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
11. Q2: How do we detect communities acting as global
authorities/hubs? 11 / 25
12. importance Global Authorities: Widespread High Importance
local authorities global authorities low widespread low importance
entropy 12 / 25
14. Global Hubs: Widespread High Dependence hubs low widespread
low dependence driven dependence entropy 14 / 25
15. 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
16. Core: Hub of dependence COIN integrated to SAP PULSAR SAP
Business One: Core dependence entropy 16 / 25
17. 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
18. 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
20. Q3: Can we exploit the model to maximise information
diffusion? 20 / 25
21. 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
22. 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
23. 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
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 24 / 25
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
26. 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
28. CBR: isolated and shrinking decreasing size rigid
member-base rising impact factor driven by self-citations group
indegree 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
29. Greedy Strategy 29
30. Group In-Degree GI = # links from outside 30
31. 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