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
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 ****