Monash University
DAI_PHD_THESIS_Final.pdf (5.59 MB)

Representation Learning for Graph-Structured Data

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posted on 2021-04-20, 02:00 authored by DAI QUOC NGUYEN
Graph-structured data is ubiquitous in science, engineering and has been successfully used in various real-life applications for social networks, molecular graphs, and biological networks. Hence, it is worth exploring prospective mechanisms to deal with the unprecedented growth in volumes and problem complexity of graph-structured data. Graph representation learning has recently emerged as a new promising paradigm, which learns a parametric mapping function that embeds nodes, subgraphs, or the entire graph into low-dimensional continuous vectors. In this thesis, we focus on developing novel and advanced graph embedding models for the two most popular types of graphs: undirected graph and knowledge graph.


Campus location


Principal supervisor

Dinh Phung

Additional supervisor 1

Tu Dinh Nguyen

Additional supervisor 2

Geoff Webb

Year of Award


Department, School or Centre

Information Technology (Monash University Clayton)

Additional Institution or Organisation

Dept of Data Science and AI, Monash University


Doctor of Philosophy

Degree Type



Faculty of Information Technology