PhD Dissertation Proposal by ZHANG Ce

Much of the data on the Web can be represented in a graph structure, ranging from social and biological to academic and Web page graphs, etc. Graph analysis recently attracts escalating research attention due to its importance and wide applicability. As a specific graph data, documents are usually connected in a graph structure. For example, Google Web pages hyperlink to other related pages, academic papers cite other papers, Facebook user profiles are connected as a social network, news articles with similar tags are linked together, etc. We call such data document graph or document network. To better make sense of the meaning within these text documents, researchers develop neural topic models. By modeling both textual content within documents and connectivity across documents, we can discover more
interpretable topics to understand the corpus and better fulfill real-world applications, such as Web page searching, news article classification, academic paper indexing, and friend recommendation based on user profiles, etc.

ZHANG Ce is a PhD Candidate in Computer Science at the SMU School of Computing and Information Systems, supervised by Prof. Hady W. Lauw. His research focuses on graph representation learning and neural topic modeling.