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。

验证关系解释：一种概率方法

Verifying Relational Explanations: A Probabilistic Approach

Graph Neural Networks (GNNs) are a powerful tool for machine learning on
graphs.GNNs combine node feature information with the graph structure by
recursively passing neural messages along edges of the input graph. However,
incorporating both graph structure and feature information leads to complex
models, and explaining predictions made by GNNs remains unsolved. Here we
propose GNNExplainer, the first general, model-agnostic approach for providing
interpretable explanations for predictions of any GNN-based model on any
graph-based machine learning task. Given an instance, GNNExplainer identifies a
compact subgraph structure and a small subset of node features that have a
crucial role in GNN's prediction. Further, GNNExplainer can generate consistent
and concise explanations for an entire class of instances. We formulate
GNNExplainer as an optimization task that maximizes the mutual information
between a GNN's prediction and distribution of possible subgraph structures.
Experiments on synthetic and real-world graphs show that our approach can
identify important graph structures as well as node features, and outperforms
baselines by 17.1% on average. GNNExplainer provides a variety of benefits,
from the ability to visualize semantically relevant structures to
interpretability, to giving insights into errors of faulty GNNs.

该研究提出了 GNNExplainer 方法，能够为任何基于 GNN 的模型和任何基于图的机器学习任务提供可解释性的预测解释，该方法能够识别关键的子图结构和节点特征，并能够生成一致而简洁的解释。