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Andrea Passerini

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#machine learning Preprint Oct 2026

Graph Additive Models disentangling feature, topology, and interactions

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 three limitations. First, node-wise relevance scores aggregate inform...

C. Álvarez-Rodríguez, Steve Azzolin, Vincenzo Marco De Luca et al. · 0 citations

Collective Node Classification through Neuro-Symbolic Integration

This work uses a recently introduced neuro-symbolic framework, to embed a standard GNN into a Relational Bayesian Network (RBN), enabling maximum a-posteriori (MAP) inference that jointly optimises node labels to match the observed local homophily structure of a graph.

Raffaele Pojer, Andrea Passerini, Kim G. Larsen et al. · 0 citations