inferring agent dynamics from social communication network hung-ching (justin) chen mark goldberg...
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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%
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