A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges

Understand

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.