Author

Vincenzo Marco De Luca

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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

Preprint Aug 2026

Overcoming Shortcut Learning in Graph Neural Networks through Active Explanation Guidance

This work introduces XIGL, an architecture-agnostic human-in-the-loop strategy for removing shortcuts from GNNs, and develops an active learning strategy for prioritizing explanations that are more likely to display shortcut behavior, lowering annotation and cognitive costs.

Taraneh Younesian, Steve Azzolin, A. Longa et al. · 0 citations