Explanations on relational data are hard to verify since the explanation structures are more complex (e.g. graphs). To verify interpretable explanations (e.g. explanations of predictions made in images, text, etc.), typically human subjects are used since it does not necessarily require a lot of expertise. However, to verify the quality of a relational explanation requires expertise and is hard to scale-up. GNNExplainer is arguably one of the most popular explanation methods for Graph Neural Networks. In this paper, we develop an approach where we assess the uncertainty in explanations generated by GNNExplainer. Specifically, we ask the explainer to generate explanations for several counterfactual examples. We generate these examples as symmetric approximations of the relational structure in the original data. From these explanations, we learn a factor graph model to quantify uncertainty in an explanation. Our results on several datasets show that our approach can help verify explanations from GNNExplainer by reliably estimating the uncertainty of a relation specified in the explanation.

翻译：关系数据上的解释因解释结构（如图）更为复杂而难以验证。通常，验证图像、文本等预测的可解释性解释时，会使用人类受试者，因为这类任务不需要大量专业知识。然而，验证关系性解释的质量需要专业知识且难以规模化。GNNExplainer可以说是图神经网络最流行的解释方法之一。本文提出了一种评估GNNExplainer生成解释中不确定性的方法。具体而言，我们要求解释器为多个反事实示例生成解释。这些示例被构建为原始数据中关系结构的对称近似。从这些解释中，我们学习一个因子图模型来量化解释的不确定性。在多个数据集上的实验结果表明，我们的方法能够通过可靠估计解释中指定关系的置信度，帮助验证GNNExplainer生成的解释。