Composite Social Networks Analyis

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering

PhD Thesis Defence

Title: "Composite Social Networks Analyis"


Mr. Erheng ZHONG


People are interconnected through online social networks ubiquitous nowadays. 
The analysis of these networks also attracts many research interests with a 
broad range of applications. Various studies have been presented to study the 
network structure as well as users' social characteristics. 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, 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 extract common knowledge from different 
networks to solve the data sparsity problem but takes care of the network 
differences. We propose 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 analyze 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, in order to 
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.

Date:			Wednesday, 22 January 2014

Time:			10:00am - 12:00noon

Venue:			Room 3494
 			Lifts 25/26

Chairman:		Prof. Weichuan Yu (ECE)

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Prof. Raymond Wong
 			Prof. Dit-Yan Yeung
 			Prof. Xiaoquan Zhang (ISOM)
                        Prof. Wei Fan (Noah's Ark Lab, HK)

**** ALL are Welcome ****