Modeling and Predicting Personal
Information Dissemination Behavior
Authors:
Ching-Yung Lin
Belle L. Tseng
Ming-Ting Sun
Speaker: Yi-Ching Huang
Outline
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Introduction
CommuntiyNet
Community Analysis
Individual Analysis
CommunityNet Applications
Conclusions
Introduction
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Not what you know, but who you know
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A social network plays a fundamental role as
a medium for the spread of information, ideas,
and influence
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We develop user-centric modeling technology
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Dynamically describe and update a PSN
Infer , predict and filter some questrions
Overview
CommunityNet
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Personal Social Network
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ERGM (p* model)
Content-Time-Relation Algorithm
Predictive Algorithm
CTR Algorithm
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Joint probabilistic model
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Sources
email content
 Sender and receiver information
 Time stamps
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CTR algorithm
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Training phase
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Input: old information from emails (content,
sender, and receiver)
Output:
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Steps:
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Estimate
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Estimate
CTR algorithm
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Testing phase
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Input: new emails with content and time
stamps
Output:
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Steps
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Estimate
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Estimate
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Update the model by incorporate the new topics
Inference, filtering, prediction
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Q1: Which is to answer a question of whom
we should send the message d to during the
time period t?
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Q2: If we receive an email, who will be
possibly the sender?
Predictive algorithm
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Use personal social network model
Use LDA combined with PSN model
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Use CTR model
Use Adaptive CTR model
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Aggregative update : t(0) ~ t(i-1)
Recent data update : t(i-n) ~ t(i-1)
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sliding window: choose efficient data
Community Analysis
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Topic analysis
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Topic distribution
Topic trend analysis
Prediction Community patterns
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share information int the community
Individual Analysis
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Role Discovery
Predicting Receivers
Inferring Senders
Adaptive Prediction
Role Discovery
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Show how people’s roles in an event
Predicting Receivers
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Infer who will possibly be the receivers by
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historic communication records
the content of the email-to-send
Inferring Senders
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Infer who will possibly be the senders by
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Person’s CommunityNet
The email content
Adaptive Prediction
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Apply adaptive algorihtm to solve the
email change problem over time
Adaptive Prediction
Community Applications
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Sensing Informal Networks
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Personal Social Network
Personal Topic-Community Network
Personal Social Capital ManagementReceiver Recommendation Demo
Personal Social Network
Personal Social Network
Personal Social Network
Personal Topic-Community
Network
Personal Social Capital ManagementReceiver Recommendation Demo
Personal Social Capital ManagementReceiver Recommendation Demo
Conclusions
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CTR algorithm incorporates contact, content,
and time information simultaneously
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CommunityNet can model and predict the
community behavior as well as personal
behavior
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Multi-modality algorithm performs better than
both the social network-based and contentbased predictions