THESIS
2014
xii, 129 pages : illustrations ; 30 cm
Abstract
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, nowadays people join multiple networks for different purposes. For example, users may
use Facebook to co...[
Read more ]
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, 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 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.
Post a Comment