1. Extract Agent-based Model from Communication Network
Hung-Ching (Justin) Chen
Matthew Francisco
Malik Magdon-Ismail
Mark Goldberg
William Wallance
RPI

2. Given a society’s communication history,
Goal
Given a society’s communication history,
can we:
Deduce something about “nature” of the society:
e.g., Do actors generally have a propensity to join small groups or large groups?
Predict the society’s future:
e.g., How many social groups are there after 3 months?
e.g., What is the distribution of group size?

3. General Approach Individual “Learn” Behavior Society’s (Micro-Laws)
History
“Predict”
(Simulate)
Society’s
Future

4. General Approach Individual “Learn” Behavior Society’s (Micro-Laws)
History
“Learn”
Individual
Behavior
(Micro-Laws)
“Predict”
(Simulate)
Society’s
Future

5. Social Networks
Individuals
(Actors) 1 2 3
Groups

6. Social Networks
Individuals
(Actors) 1 2
- Join
- Leave
Groups 3

7. Social Networks Individuals (Actors) - Join Groups - Leave - Disappear4 1
- Join
- Leave 2
Groups
- Disappear
- Appear
- Re-appear 3

8. Society’s History

9. General Approach Individual “Learn” Behavior Society’s (Micro-Laws)
History
“Predict”
(Simulate)
Society’s
Future

10. Join / Leave / Do Nothing
Modeling of Dynamics
Parameters
History
Groups & Individuals
Join / Leave / Do Nothing
Micro-Law
# 1
# 2
# N …
Actions

11. Actor X likes to join groups.
Example of Micro-Law
Actor X likes to join groups.
SMALL
LARGE
Parameter

12. ViSAGE Virtual Simulation and Analysis of Group Evolution
State: Properties of Actors and Groups
State
Decide
Actors’ Action
Normative
Action
State
State update
Actor
Choice
State
Feedback
to Actors
Process Actors’ Action
Real Action

13. General Approach Individual “Learn” Behavior Society’s (Micro-Laws)
History
“Predict”
(Simulate)
Society’s
Future

14. Learning ? ? Parameters #1 in Micro-Laws Learn Parameters #2 in
Communications
Parameters #1 in
Micro-Laws ?
Learn
Parameters #2 in
Micro-Laws ?

15. Groups & Group Evolution
Communications
Groups
Evolution
Groups:
Overlapping
clustering
Group
evolution:
Matching

16. Actor’s Types Leader: prefer small group size and is most ambitious
Socialite: prefer medium group size and is medium ambitious
Follower: prefer large group size and is least ambitious

17. Learning Actors’ Type Maximum log-likelihood learning algorithm
Cluster algorithm
EM algorithm

18. Testing Simulation Data

19. Testing Real Data Cluster Algorithm EM Algorithm Learned Actors’ Types
Leader
Socialite
Follower
Number of Actor
822
550
156
Percentage
53.8%
36.0%
10.2% EM
Algorithm
Learned Actors’ Types
Leader
Socialite
Follower
Number of Actor
532
368
628
Percentage
34.8%
24.1%
41.1%

20. General Approach Individual “Learn” Behavior Society’s (Micro-Laws)
History
“Predict”
(Simulate)
Society’s
Future

21. Testing & Simulations
Micro-Laws
&
Parameters
# 1
Simulate
# 2

22. Prediction

23. Prediction

24. Future Work Test Other Predictions
e.g., membership in a particular group
Learn from Other Real Data
e.g., s and blogs

25. Questions?