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（GNN）的可解释性是过去几年中新兴的研究领域。本文探讨了在GNN对节点进行分类时，如何确定每个邻居的重要性，以及如何衡量这一特定任务的性能。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GNN领域中，基于梯度的技术所提供的解释几乎没有差异。此外，当使用无自环的GNN时，许多可解释性技术无法识别重要邻居。