GNN Explainers 2.0: User-centric and Data-driven Insights

Graph neural networks (GNNs) are deep learning models designed for graph-structured data that have achieved strong results across domains--social networks, knowledge graphs, bioinformatics, transportation, World Wide Web, and finance--on tasks such as node and graph classification, link prediction, entity resolution, question answering, recommendation, and fraud detection. Explaining the decisions of high-performing, yet ''black-box'' GNNs remains both challenging and essential. The initial five years have produced tremendous progress with many GNN explainers (e.g., GNNExplainer, PGExplainer, SubgraphX, PGMExplainer, GraphLime, GCFExplainer, CF2, GNN-LRP) that identify the influential nodes, edges, subgraphs, and features aiming to explain the output of GNNs.