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.

通过生成对称近似的对抗性样本，我们开发了一种方法来评估 GNNExplainer 生成的解释中的不确定性，通过学习一个因子图模型来量化解释的不确定性，我们的结果表明我们的方法可以可靠地估计解释中指定关系的不确定性。