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

2. 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 Behavior (Micro-Laws) “Learn” Society’s History “Predict” (Simulate) Society’s Future

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

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

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

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

8. Society’s History

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

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

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

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

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

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

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

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

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

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

22. Prediction

23. Prediction

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

25. Questions?