Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition. Most existing methods generate explanations by identifying a subgraph of an input graph that has a strong correlation with the prediction. These explanations are not robust to noise because independently optimizing the correlation for a single input can easily overfit noise. Moreover, they do not align well with human intuition because removing an identified subgraph from an input graph does not necessarily change the prediction result. In this paper, we propose a novel method to generate robust counterfactual explanations on GNNs by explicitly modelling the common decision logic of GNNs on similar input graphs. Our explanations are naturally robust to noise because they are produced from the common decision boundaries of a GNN that govern the predictions of many similar input graphs. The explanations also align well with human intuition because removing the set of edges identified by an explanation from the input graph changes the prediction significantly. Exhaustive experiments on many public datasets demonstrate the superior performance of our method.

翻译：在高吸量应用中大规模部署图形神经网络(GNNs)产生强烈的解释需求,这种解释对噪音和人类直觉非常有力。大多数现有方法通过确定一个与预测密切相关的输入图的子集来产生解释。这些解释对噪音并不有力,因为独立优化单一输入的关联性可以很容易地超过噪音。此外,它们与人类直觉不相符,因为从输入图中删除一个确定的子集不一定改变预测结果。在本文中,我们提出了一个新颖的方法,通过在类似输入图上明确模拟GNNs的共同决定逻辑来产生关于GNes的有力的反事实解释。我们的解释自然地对噪音很可靠,因为它们来自一个管理许多类似输入图预测的GNN的通用决定边界。解释也与人类直觉很吻合,因为从输入图的解释中去除所发现的边缘对预测结果作了显著的改变。许多公共数据集的探索实验显示了我们方法的优异性。