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...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 47(2025), 4 vom: 15. Apr., Seite 2882-2896
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| 1. Verfasser: |
Zhou, Jiajun
(VerfasserIn) |
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| Weitere Verfasser: |
Gong, Shengbo,
Chen, Xuanze,
Xie, Chenxuan,
Yu, Shanqing,
Xuan, Qi,
Yang, Xiaoniu |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2025
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article |
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