Clarify Confused Nodes via Separated Learning
Graph neural networks (GNNs) have achieved remarkable advances in graph-oriented tasks. However, real-world graphs invariably contain a certain proportion of heterophilous nodes, challenging the homophily assumption of traditional GNNs and hindering their performance. Most existing studies continue...
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Détails bibliographiques
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 47(2025), 4 vom: 15. Apr., Seite 2882-2896
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| Auteur principal: |
Zhou, Jiajun
(Auteur) |
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| Autres auteurs: |
Gong, Shengbo,
Chen, Xuanze,
Xie, Chenxuan,
Yu, Shanqing,
Xuan, Qi,
Yang, Xiaoniu |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2025
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence
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| Sujets: | Journal Article |
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