Knowledge Transfer in Composite Social Networks

PhD Thesis Proposal Defence


Title: "Knowledge Transfer in Composite Social Networks"

by

Mr. Erheng ZHONG


Abstract:

With the growth of online social media, social network analysis has 
attracted many research interests with a broad range of applications. 
Various studies have been presented to study the network structure as well 
as users' social behaviors. Despite of their success, most previous 
research works focus on analyzing individual networks. However, data in 
individual networks can be quite sparse and each individual social network 
may reflect only partial aspects of users' social behaviors. Building 
models on such networks may overfit the rare observations and fail to 
capture the whole picture of users' social interests. In reality, nowadays 
people join multiple networks for different purposes. For example, users 
may use Facebook to connect with their friends, talk with their families 
on Skype and follow celebrities on Twitter, etc. Thus, different networks 
are correlated with each other and nested together as composite social 
networks by the shared users. If we consider these users as the bridge, 
fragmented knowledge in individual networks can be utilized collectively 
to build more accurate models and obtain comprehensive understandings of 
users' social behaviors.

In this research, our main idea is to employ transfer learning, that 
extracts common knowledge from different networks to solve the data 
sparsity problem but takes care of the network differences. We propose to 
build a general framework, known as ComSoc, based on hierarchical Bayesian 
models, by encoding common knowledge and network differences as latent 
factors. Based on this framework, we will research knowledge transfer in 
composite social networks from four major aspects: 1). how to model the 
composite network structures; 2). how to model the dynamics and network 
co-evolution; 3). how to adaptively predict users' social behaviors across 
social medias; and 4). how to measure users' distances specifically in 
different networks. We will use large-scale social networking datasets, to 
carry out this research. We will demonstrate how our ComSoc framework can 
be instantiated for solving these four problems. Finally, to handle big 
data, we propose a novel parallel framework that makes the model inference 
efficient. The proposal will also discuss some difficulties which have 
been tackled by related works and our preliminary feasibility study, and 
then point out some ongoing research issues for extensive investigation.


Date:			Wednesday, 11 September 2013

Time:                   2:00pm - 4:00pm

Venue:                  Room 3402
                         lifts 17/18

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Dr. Raymond Wong (Chairperson)
 			Dr. Sunghun Kim
 			Prof. Nevin Zhang


**** ALL are Welcome ****