Explainability in Graph Neural Networks (GNNs) is a new field growing in the last few years. In this publication we address the problem of determining how important is each neighbor for the GNN when classifying a node and how to measure the performance for this specific task. To do this, various known explainability methods are reformulated to get the neighbor importance and four new metrics are presented. Our results show that there is almost no difference between the explanations provided by gradient-based techniques in the GNN domain. In addition, many explainability techniques failed to identify important neighbors when GNNs without self-loops are used.

翻译：图神经网络的可解释性是一个近年来快速发展的新兴领域。本文旨在解决两个核心问题：在节点分类任务中，每个邻居节点对图神经网络的重要性如何量化，以及如何评估这一特定任务的可解释性性能。为此，我们重新表述了多种已知的可解释性方法以获取邻居重要性，并提出了四种新的评估指标。结果表明，在图神经网络领域中，基于梯度的技术所提供的解释几乎不存在差异。此外，当使用不含自环的图神经网络时，许多可解释性方法未能有效识别重要邻居节点。