From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning

ArXi:2605.06814v1 Announce Type: new Graph Neural Networks (GNNs) achieve high performance but can be opaque to humans, making it difficult to understand and compare the many proposed architectures. While existing explainability methods attribute individual predictions to nodes, edges, or features, they do not provide architectural transparency or explain the fundamental performance gap between simple and complex models. To address this limitation, we