Graph Additive Models disentangling feature, topology, and interactions
arXiv:2610.20662v1 Announce Type: new Abstract: Graph Neural Additive Networks (GNANs) have emerged as effective, interpretable-by-design architectures for graph learning. They achieve transparency by combining per-feature shape functions with simple distance-based reweighting. However, GNANs have…
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- 2026-10-16 04:00 · arXiv stat.ML
Graph Additive Models disentangling feature, topology, and interactions