Enhanced sample selection in graph contrastive learning with attribute-structure fusion

Graph contrastive learning (GCL) has emerged as a crucial framework for advancing self-supervised graph representation learning. However, a critical challenge in GCL stems from insufficient positive sample selection, which results in limited supervision signals and significantly hinders the represen...

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Bibliografiska uppgifter

Huvudupphov:	Yafang Li, Haoxuan Zhang, Caiyan Jia, Baokai Zu, Hongyuan Wang
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	Elsevier 2026-07-01
Serie:	Array
Ämnen:	Graph contrastive learning Graph representation learning Positive sample selection Self-supervised learning
Länkar:	http://www.sciencedirect.com/science/article/pii/S2590005626001189
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