Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs
<|reference_start|>Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs: As Graph Neural Networks (GNNs) become more pervasive, it becomes paramount to build robust tools for computing explanations of their predictions. A key desideratum is that these explanations are faithful, i.e., that they portray an accurate picture of the GNN's reasoning process. A number of different faithfulness metrics exist, begging the question of what faithfulness is exactly, and what its properties are. We begin by showing that existing metrics are not interchangeable -- i.e., explanations attaining high faithfulness according to one metric may be unfaithful according to others -- and can be systematically insensitive to important properties of the explanation, and suggest how to address these issues. We proceed to show that, surprisingly, optimizing for faithfulness is not always a sensible design goal. Specifically, we show that for injective regular GNN architectures, perfectly faithful explanations are completely uninformative. The situation is different for modular GNNs, such as self-explainable and domain-invariant architectures, where optimizing faithfulness does not compromise informativeness, and is also unexpectedly tied to out-of-distribution generalization.<|reference_end|>
@article{azzolin2024perks,
    title={Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and
  Domain Invariant GNNs},
    author={Steve Azzolin, Antonio Longa, Stefano Teso, Andrea Passerini},
    journal={arXiv preprint arXiv:2406.15156},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15156},
    primaryClass={cs.LG cs.AI}
}