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Arxiv
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Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs
- Graph Neural Networks (GNNs) need reliable tools for explaining their predictions.
- Existing faithfulness metrics are not interchangeable.
- Optimizing for faithfulness may not always be a sensible design goal for regular GNN architectures.
- Faithfulness is tightly linked to out-of-distribution generalization.
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