Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs

• URL: http://arxiv.org/abs/2406.15156v1
• Date: Fri, 21 Jun 2024 14:01:23 GMT
• Title: Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs
• Authors: Steve Azzolin, Antonio Longa, Stefano Teso, Andrea Passerini,
• Abstract summary: A key desideratum is that 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 show that, surprisingly, optimizing for faithfulness is not always a sensible design goal.
• Score: 18.33293911039292
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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