Extract Agent-based Model from

Communication Network

Hung-Ching (Justin) ChenMatthew Francisco

Malik Magdon-IsmailMark Goldberg

William WallanceRPI

Goal

n Deduce something about “nature” of the society:n e.g., Do actors generally have a propensity to

join small groups or large groups?n Predict the society’s future:

n e.g., How many social groups are there after 3 months?

n 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” IndividualBehavior

(Micro-Laws)

General Approach

Society’s History

Society’s Future

“Predict”(Simulate)

“Learn” IndividualBehavior

(Micro-Laws)

Social Networks• Individuals (Actors)

• Groups

12

3

Social Networks• Individuals (Actors)

• Groups

1 2

3

- Join - Leave

4

Social Networks• Individuals (Actors)

• Groups

1

3

- Join - Leave

- Disappear - Appear

2

- Re-appear

Society’s History

General Approach

Society’s History

Society’s Future

“Predict”(Simulate)

“Learn” IndividualBehavior

(Micro-Laws)

Modeling of Dynamics

Micro-Law# 1

Micro-Law# 2

Micro-Law# N

…

Parameters HistoryGroups & Individuals

Actions Join / Leave / Do Nothing

Example of Micro-Law

Actor X likes to join groups.

Parameter

SMALLLARGE

ViSAGEVirtual Simulation and Analysis of Group

Evolution

Real Action

ActorChoice

State: Properties of Actors and Groups

Decide Actors’ Action

Process Actors’ Action

Feedbackto Actors

State

StateState update

NormativeAction

State

General Approach

Society’s History

Society’s Future

“Predict”(Simulate)

“Learn” IndividualBehavior

(Micro-Laws)

Learning

Learn

Parameters #1in

Micro-Laws?

?

Communications

Parameters #2in

Micro-Laws

Groups & Group Evolution

Communications

Groups: Overlappingclustering

GroupsEvolution

Groupevolution: Matching

Actor’s Typesn Leader: prefer small group size and is most

ambitiousn Socialite: prefer medium group size and is

medium ambitiousn Follower: prefer large group size and is

least ambitious

Learning Actors’ Type

n Maximum log-likelihood learning algorithmn Cluster algorithmn EM algorithm

Testing Simulation Data

Testing Real DataCluster

AlgorithmLearned Actors’ Types

Leader Socialite FollowerNumber of Actor 822 550 156

Percentage 53.8% 36.0% 10.2%

EMAlgorithm

Learned Actors’ TypesLeader Socialite Follower

Number of Actor 532 368 628Percentage 34.8% 24.1% 41.1%

General Approach

Society’s History

Society’s Future

“Predict”(Simulate)

“Learn” IndividualBehavior

(Micro-Laws)

Testing & Simulations

Micro-Laws&

Parameters# 1

Simulate

Micro-Laws&

Parameters# 2

Simulate

Prediction

Prediction

Future Work

n Test Other Predictionsn e.g., membership in a particular group

n Learn from Other Real Datan e.g., emails and blogs

Questions?