Inferring Agent Dynamics from

Social Communication

Network

Inferring Agent Dynamics from

Social Communication

NetworkHung-Ching (Justin) Chen

Mark GoldbergMalik Magdon-IsmailWilliam A. Wallance

RPI, Troy, NY

Hung-Ching (Justin) ChenMark Goldberg

Malik Magdon-IsmailWilliam A. Wallance

RPI, Troy, NY

GoalGoal

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 ApproachGeneral Approach

Society’s History

Society’s Future

“Predict”(Simulate)

“Learn” IndividualBehavior

(Micro-Laws)

General ApproachGeneral Approach

Society’s History

Society’s Future

“Predict”(Simulate)

“Learn” IndividualBehavior

(Micro-Laws)

Social NetworksSocial Networks• Individuals (Actors)

• Groups

1

2

3

Social NetworksSocial Networks• Individuals (Actors)

• Groups

1 2

3

- Join - Leave

4

Social NetworksSocial Networks• Individuals (Actors)

• Groups

1

3

- Join - Leave

- Disappear - Appear

2

- Re-appear

Society’s HistorySociety’s History

General ApproachGeneral Approach

Society’s History

Society’s Future

“Predict”(Simulate)

“Learn” IndividualBehavior

(Micro-Laws)

Modeling of Dynamics

Modeling of Dynamics

Micro-Law# 1

Micro-Law# 2

Micro-Law# N

…

Parameters HistoryGroups & Individuals

Actions Join / Leave / Do Nothing

Example of Micro-Law

Example of Micro-Law

Actor X likes to join groups.

Parameter

SMALLLARGE

ViSAGEVirtual Simulation and Analysis of Group

Evolution

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 ApproachGeneral Approach

Society’s History

Society’s Future

“Predict”(Simulate)

“Learn” IndividualBehavior

(Micro-Laws)

LearningLearning

Learn

Parameters #1in

Micro-Laws?

?

Communications

Parameters #2in

Micro-Laws

Groups & Group Evolution

Groups & Group Evolution

Communications

Groups: Overlappingclustering

GroupsEvolution

Groupevolution: Matching

LearningLearning

Parametersin

Micro-Laws

GroupsEvolution

EM Algorithms

General ApproachGeneral Approach

Society’s History

Society’s Future

“Predict”(Simulate)

“Learn” IndividualBehavior

(Micro-Laws)

Testing & SimulationsTesting &

SimulationsMicro-Laws

&Parameters

# 1

Simulate

Micro-Laws&

Parameters# 2

Simulate

Actor’s TypesActor’s Types

n Leader: prefer small group size and is most ambitious

n Socialite: prefer medium group size and is medium ambitious

n Follower: prefer large group size and is least ambitious

Learning Actors’ Type

Learning Actors’ Type

n Maximum log-likelihood learning algorithm

n EM algorithm

n Cluster algorithm

Testing Simulation Data

Testing Simulation Data

Testing Real DataTesting Real DataCluster

Algorithm

Learned Actors’ Types

Leader Socialite Follower

Number of Actor 822 550 156

Percentage 53.8% 36.0% 10.2%

EMAlgorithm

Learned Actors’ Types

Leader Socialite Follower

Number of Actor 532 368 628

Percentage 34.8% 24.1% 41.1%

PredictionPrediction

PredictionPrediction

Future WorkFuture Work

n Test Other Predictions

n e.g., membership in a particular group

n Learn from Other Real Data

n e.g., emails and blogs

Questions?Questions?

Enron EmailEnron EmailCluster

Algorithm

Learned Actors’ Types

Leader Socialite Follower

Number of Actor 28 50 76

Percentage 18.2% 32.5% 49.3%

EMAlgorithm

Learned Actors’ Types

Leader Socialite Follower

Number of Actor 24 62 68

Percentage 15.6% 40.2% 44.2%

Movie NewsgroupMovie NewsgroupCluster

Algorithm

Learned Actors’ Types

Leader Socialite Follower

Number of Actor 822 550 156

Percentage 53.8% 36.0% 10.2%

EMAlgorithm

Learned Actors’ Types

Leader Socialite Follower

Number of Actor 532 368 628

Percentage 34.8% 24.1% 41.1%