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