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M odeling and Predicting Personal Information Dissemination Behavior. A uthors: C hing-Yung Lin B elle L. Tseng Ming-Ting Sun Speaker: Yi-Ching Huang. O utline. I ntroduction CommuntiyNet Community Analysis Individual Analysis CommunityNet Applications Conclusions. I ntroduction. - PowerPoint PPT Presentation
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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
Outline Introduction CommuntiyNet Community Analysis Individual Analysis CommunityNet Applications Conclusions
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
Overview
CommunityNet Personal Social Network
ERGM (p* model)
Content-Time-Relation Algorithm Predictive Algorithm
CTR Algorithm Joint probabilistic model
Sourcesemail contentSender and receiver informationTime stamps
CTR algorithm Training phase
Input: old information from emails (content, sender, and receiver)
Output:
Steps: Estimate
Estimate
CTR algorithm Testing phase
Input: new emails with content and time stamps
Output: Steps
Estimate Estimate Update the model by incorporate the new topics
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?
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
Community Analysis Topic analysis
Topic distribution Topic trend analysis
Prediction Community patterns share information int the community
Individual Analysis Role Discovery Predicting Receivers Inferring Senders Adaptive Prediction
Role Discovery Show how people’s roles in an event
Predicting Receivers Infer who will possibly be the receivers by
historic communication records the content of the email-to-send
Inferring Senders
Infer who will possibly be the senders by Person’s CommunityNet The email content
Adaptive Prediction Apply adaptive algorihtm to solve the
email change problem over time
Adaptive Prediction
Community Applications Sensing Informal Networks
Personal Social Network Personal Topic-Community Network
Personal Social Capital Management-Receiver Recommendation Demo
Personal Social Network
Personal Social Network
Personal Social Network
Personal Topic-Community Network
Personal Social Capital Management-Receiver Recommendation Demo
Personal Social Capital Management-Receiver Recommendation Demo
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