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.

翻译：图神经网络（GNNs）在社区检测和分子分类任务中表现出色。反事实解释（CE）通过提供反例来克服黑盒模型的可解释性局限。随着图学习领域日益受到关注，本文聚焦于GNN的反事实解释概念。我们系统分析了现有技术，提出了分类体系、统一符号规范，并梳理了基准数据集与评估指标。本文详细讨论了十四种方法及其评估流程、二十二个数据集和十九项评估指标。我们将大部分方法集成至GRETEL库中进行实证评估，以深入理解其优势与缺陷。最后，我们指出了当前面临的开放挑战与未来研究方向。