Graph Counterfactual

Graph counterfactual explanations (GCEs) aim to understand decisions made by graph neural networks (GNNs) by generating minimally altered input graphs that lead to different predictions. Current research focuses on developing robust and efficient GCE methods, often employing graph autoencoders and semi-supervised learning techniques to generate these counterfactual graphs, and evaluating them using standardized frameworks. This field is crucial for improving the transparency and trustworthiness of GNNs, particularly in high-stakes applications where understanding model decisions is paramount, such as drug discovery or social network analysis.