Graph neural networks (GNNs) are quickly becoming the standard approach for
learning on graph structured data across several domains, but they lack
transparency in their decision-making. Several perturbation-based approaches
have been developed to provide insights into the decision making process of
GNNs. As this is an early research area, the methods and data used to evaluate
the generated explanations lack maturity. We explore these existing approaches
and identify common pitfalls in three main areas: (1) synthetic data generation
process, (2) evaluation metrics, and (3) the final presentation of the
explanation. For this purpose, we perform an empirical study to explore these
pitfalls along with their unintended consequences and propose remedies to
mitigate their effects.

该研究论文探讨了关于图神经网络（GNNs）在决策过程中透明度不足的问题，并针对生成 GNNs 解释的方法和数据的评估，研究了三个主要领域的普遍问题：（1）合成数据生成过程，（2）评估指标，以及（3）解释的最终呈现。此外，本文进行了一项实证研究，探讨了这些问题的意外后果，并提出了缓解这些问题的建议。