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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Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 35; H. 6; S. 5879 - 5900
Hauptverfasser:	Liu, Yixin, Jin, Ming, Pan, Shirui, Zhou, Chuan, Zheng, Yu, Xia, Feng, Yu, Philip S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Computer vision Data models Deep learning graph analytics graph neural networks graph representation learning Graphs Manuals Natural language processing Robustness (mathematics) Self-supervised learning Supervised learning Task analysis Taxonomy
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
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