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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| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Elsevier
2026-07-01
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| coleção: | Array |
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| Acesso em linha: | http://www.sciencedirect.com/science/article/pii/S2590005626001189 |
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