论文标题
图形反事实解释的调查：定义，方法，评估和研究挑战
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges
论文作者
论文摘要
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）提供了反例以克服黑盒模型的透明度限制。由于图表学习的关注不断增加，我们专注于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，他们的评估协议，22个数据集和19个指标。我们将大多数方法整合到Gretel文库中，以进行经验评估，以了解其优势和陷阱。我们重点介绍了开放的挑战和未来的工作。
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.
