Full metadata
Title
Data-Efficient Graph Learning
Description
Graph-structured data, ranging from social networks to financial transaction networks, from citation networks to gene regulatory networks, have been widely used for modeling a myriad of real-world systems. As a prevailing model architecture to model graph-structured data, graph neural networks (GNNs) has drawn much attention in both academic and industrial communities in the past decades. Despite their success in different graph learning tasks, existing methods usually rely on learning from ``big'' data, requiring a large amount of labeled data for model training. However, it is common that real-world graphs are associated with ``small'' labeled data as data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph machine learning (Graph ML) with low-cost human supervision for low-resource settings where limited or even no labeled data is available. This dissertation investigates a new research field -- Data-Efficient Graph Learning, which aims to push forward the performance boundary of graph machine learning (Graph ML) models with different kinds of low-cost supervision signals. To achieve this goal, a series of studies are conducted for solving different data-efficient graph learning problems, including graph few-shot learning, graph weakly-supervised learning, and graph self-supervised learning.
Date Created
2023
Contributors
- Ding, Kaize (Author)
- Liu, Huan (Thesis advisor)
- Xue, Guoliang (Committee member)
- Yang, Yezhou (Committee member)
- Caverlee, James (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
161 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.187374
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: Ph.D., Arizona State University, 2023
Field of study: Computer Science
System Created
- 2023-06-06 07:26:37
System Modified
- 2023-06-06 07:26:43
- 1 year 5 months ago
Additional Formats