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.

图神经网络（GNNs）中的可解释性是一个新兴领域，在本文中我们讨论了确定每个邻居对于GNN在对节点进行分类时的重要性以及如何度量该特定任务性能的问题。为了做到这一点，我们重新构造了各种已知的可解释性方法来获取邻居的重要性，并提出了四个新的度量指标。我们的结果显示，在GNN领域中，基于梯度的技术提供的解释几乎没有区别。此外，当使用没有自环的GNN时，许多可解释性技术未能识别到重要的邻居。

评估图神经网络的邻居解释性