Deep Graph Neural Networks for Unsupervised Graph Learning
- Publication Type:
- Thesis
- Issue Date:
- 2020
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Graphs are widely used to represent networked data, which contains complex relationships among individuals, and therefore hard to represented by traditional flat-table or vector format. Network applications, like social networks or citation networks, have been developing rapidly in recent years. Consequently, graph learning has also attracted much more attention.
Unsupervised graph learning is an important and challenging branch of the field since label information is useful but usually not easily accessible. Associated downstream tasks of unsupervised graph learning may include clustering, link prediction, etc.
In this thesis, we investigate previous graph learning methods, summarize their limitations, and try to confront the raised challenges to perform more effective graph learning, with deep graph neural networks in an unsupervised manner. We propose four frameworks that are validated with unsupervised tasks like clustering in the experiments.
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