Graph Additive Models disentangling feature, topology, and interactions

Clara Alvarez-Rodriguez, Steve Azzolin, Vincenzo Marco De Luca +3

Graph Neural Additive Networks (GNANs) have emerged as effective, interpretable-by-design architectures for graph learning. They achieve transparency by combining per-feature shape…