1. Inferring Agent Dynamics from Social Communication Network Hung-Ching (Justin) Chen Mark Goldberg Malik Magdon-Ismail William A. Wallance RPI, Troy, NY Hung-Ching (Justin) Chen Mark Goldberg Malik Magdon-Ismail William A. Wallance RPI, Troy, NY

2. Goal 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? Given a society’s communication history, can we:

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

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

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

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

7. 4 Social Networks Individuals (Actors) Groups 1 3 - Join - Leave - Disappear - Appear 2 - Re-appear

8. Society’s History

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

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

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

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

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

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

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

16. Learning Parameters in Micro-Laws Groups Evolution EM Algorithms

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

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

19. 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

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

21. Testing Simulation Data

22. Testing Real Data Cluster Algorithm Learned Actors’ Types LeaderSocialiteFollower Number of Actor822550156 Percentage53.8%36.0%10.2% EM Algorithm Learned Actors’ Types LeaderSocialiteFollower Number of Actor532368628 Percentage34.8%24.1%41.1%

23. Prediction

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

26. Questions?

27. Enron Email Cluster Algorithm Learned Actors’ Types LeaderSocialiteFollower Number of Actor285076 Percentage18.2%32.5%49.3% EM Algorithm Learned Actors’ Types LeaderSocialiteFollower Number of Actor246268 Percentage15.6%40.2%44.2%

28. Movie Newsgroup Cluster Algorithm Learned Actors’ Types LeaderSocialiteFollower Number of Actor822550156 Percentage53.8%36.0%10.2% EM Algorithm Learned Actors’ Types LeaderSocialiteFollower Number of Actor532368628 Percentage34.8%24.1%41.1%