How Faithful are Self-Explainable GNNs?

• URL: http://arxiv.org/abs/2308.15096v1
• Date: Tue, 29 Aug 2023 08:04:45 GMT
• Title: How Faithful are Self-Explainable GNNs?
• Authors: Marc Christiansen, Lea Villadsen, Zhiqiang Zhong, Stefano Teso, Davide Mottin
• Abstract summary: Self-explainable graph neural networks (GNNs) aim at achieving the same in the context of graph data. We analyze the faithfulness of several self-explainable GNNs using different measures of faithfulness.
• Score: 14.618208661185365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-explainable deep neural networks are a recent class of models that can output ante-hoc local explanations that are faithful to the model's reasoning, and as such represent a step forward toward filling the gap between expressiveness and interpretability. Self-explainable graph neural networks (GNNs) aim at achieving the same in the context of graph data. This begs the question: do these models fulfill their implicit guarantees in terms of faithfulness? In this extended abstract, we analyze the faithfulness of several self-explainable GNNs using different measures of faithfulness, identify several limitations -- both in the models themselves and in the evaluation metrics -- and outline possible ways forward.

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