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.

自解释深度神经网络是最近的一类模型，可以输出与模型推理相一致的直观解释，从而在表达能力和可解释性之间实现了一定的进步。自解释图神经网络通过图数据的方式实现了相同的目标。本文对几种自解释图神经网络的忠诚度进行了分析，使用了不同的忠诚度度量方式，并指出了模型本身和评估指标的一些局限性，并提出了进一步研究的可能方向。