DAI_PHD_THESIS_Final.pdf (5.59 MB)

Representation Learning for Graph-Structured Data

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thesis
posted on 20.04.2021, 02:00 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.

History

Campus location

Australia

Principal supervisor

Dinh Phung

Additional supervisor 1

Tu Dinh Nguyen

Additional supervisor 2

Geoff Webb

Year of Award

2021

Department, School or Centre

Clayton School of IT

Additional Institution or Organisation

Dept of Data Science and AI, Monash University

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Exports