  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.
