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

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:

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

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

Social Networks Individuals (Actors) Groups 1 2 3

Social Networks Individuals (Actors) Groups Join - Leave

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

Society’s History

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

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

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

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

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

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

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

Learning Parameters in Micro-Laws Groups Evolution EM Algorithms

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

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

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 EM algorithm Cluster algorithm

Testing Simulation Data

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

Prediction

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

Questions?

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

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