Reinforced Causal Explainer for Graph Neural Networks
Explainability is crucial for probing graph neural networks (GNNs), answering questions like "Why the GNN model makes a certain prediction?". Feature attribution is a prevalent technique of highlighting the explanatory subgraph in the input graph, which plausibly leads the GNN model to mak...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 2 vom: 11. Feb., Seite 2297-2309
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| 1. Verfasser: |
Wang, Xiang
(VerfasserIn) |
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| Weitere Verfasser: |
Wu, Yingxin,
Zhang, An,
Feng, Fuli,
He, Xiangnan,
Chua, Tat-Seng |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2023
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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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