Graph Self-Supervised Learning: A Survey

Graph Self-Supervised Learning: A Survey

Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in shortcomings including heavy label reliance, poor generalization, and weak robustness. To address these issues, self-supervised learning (SSL), w...

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Bibliographic Details
Main Authors: Jin, M. (Author), Liu, Y. (Author), Pan, S. (Author), Xia, F. (Author), Yu, P. (Author), Zheng, Y. (Author), Zhou, C. (Author)
Format: Article
Language:English
Published: IEEE Computer Society 2022
Subjects:
Benchmarking
Data models
deep learning
Deep learning
Deep neural networks
graph analytics
Graph data
graph neural networks
Graph neural networks
Graph representation
graph representation learning
Graph representation learning
Graph-analytic
Job analysis
Manual
Manuals
Natural language processing
Natural language processing systems
Open systems
Self-supervised learning
Supervised learning
Task analysis
Taxonomies
Taxonomy
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