Advancement in graph data mining: applications in unsupervised, continual, and few-shot learning

Graph mining has proven to be extremely useful in analysing features and properties of real-world graphs. This enables a number of tasks including the prediction and evaluation of how information varies with changes in the link structure, generating and building models to extract properties such as...

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Bibliographic Details
Main Author: Rakaraddi, Appan
Other Authors: Lam Siew Kei
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176227
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Institution: Nanyang Technological University
Language: English