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
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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
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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?
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5. Q1: How can we model influence between communities?
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6. Methodology: COIN
What
impacts
depends on
How
T
centrality
communities
communities
membership
communities
actors
actors
communities
impact
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7. 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
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8. Experiments
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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
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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
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11. Q2: How do we detect communities acting as global authorities/hubs?
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12. importance
Global Authorities:
Widespread High Importance
local
authorities
global
authorities
low
widespread
low
importance entropy
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13. Moderators: Authority of
●
importance
2.0
Moderators
●
1.5
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0.60
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0.65
0.70
importance entropy
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14. Global Hubs:
Widespread High Dependence
hubs
low
widespread low
dependence
driven
dependence entropy
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15. After Hours: Hub of
dependence
●
10
After Hours
5
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dependence entropy
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16. Core: Hub of
dependence
 COIN integrated to SAP PULSAR
SAP Business
One: Core
dependence entropy
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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
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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
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19. 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
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0.00
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0.0
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loose
exporters
●
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●
IJCAI
0.5
1.0
importance
1.5
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20. Q3: Can we exploit the model to maximise information diffusion?
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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?
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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
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23. Selection
user activation fraction (a)
COIN Optimises Information Diffusion
0.05
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0.04
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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)
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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
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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
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26. Personal Issues and Moderators
membership
indegree
1.00
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0.75
ld
12
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30
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8
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20
0.50
0.25
10
0.00
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4
0
PI
PIM
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group
PI
PIM
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0
PI
PIM
PI
PIM
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27. CBR community: isolated
CBR
in−, out−flow
1.6
1.2
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out−flow
in−flow
introspection
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out−flow
in−flow
introspection
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0.8
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1996
1997
1998
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2000
2001
2002
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2003
2004
2005
2006
2007
2008
year
JELIA
in−, out−flow
3
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1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
year
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28. CBR: isolated and shrinking
• decreasing size
• rigid member-base
rising impact factor
driven by self-citations
●
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group in−degree
160
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size
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140
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120
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120
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80
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40
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1.00
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impact factor
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0.75
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0.50
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0.25
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0.00
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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
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29. Greedy Strategy
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30. Group In-Degree
GI = # links from outside
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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

32. Rise of Hubs and Authorities in Boards
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33. Exporters and Introspective Communities
33