Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide counter-examples to overcome the transparency limitations of black-box models. Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs. We analysed the SoA to provide a taxonomy, a uniform notation, and the benchmarking datasets and evaluation metrics. We discuss fourteen methods, their evaluation protocols, twenty-two datasets, and nineteen metrics. We integrated the majority of methods into the GRETEL library to conduct an empirical evaluation to understand their strengths and pitfalls. We highlight open challenges and future work.

图神经网络（GNN）在社区检测和分子分类方面表现良好。反事实解释（CE）提供了反例来克服黑盒模型的透明度限制。由于图学习日益受到关注，我们重点关注 GNN 的 CE 概念。我们分析了 SoA，以提供分类法、统一符号以及基准数据集和评估指标。我们讨论了十四种方法、它们的评估协议、二十二个数据集和十九个指标。我们将大多数方法集成到 GRETEL 库中进行实证评估，以了解它们的优点和缺陷。我们强调开放的挑战和未来的工作。