Generalizing Graph Neural Networks on Out-of-Distribution Graphs
Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training graphs and testing graphs, inducing the degeneration of the generalization ability of GNNs in Out-Of-Distribution (OOD) settings. The fundamental reason for such degeneration is that most...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 46(2023), 1 vom: 03. Jan., Seite 322-337
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| 1. Verfasser: |
Fan, Shaohua
(VerfasserIn) |
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| Weitere Verfasser: |
Wang, Xiao,
Shi, Chuan,
Cui, Peng,
Wang, Bai |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2024
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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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