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

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?

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

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

Social Networks Individuals (Actors) 1 2 3 Groups

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

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

Society’s History

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

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

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

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

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

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

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

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

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

Testing Simulation Data

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%

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

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

Prediction

Prediction

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

Questions?