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