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Representation Learning for Graph-Structured Data

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posted on 20.04.2021, 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

Clayton School of IT

Additional Institution or Organisation

Dept of Data Science and AI, Monash University


Doctor of Philosophy

Degree Type