Author

Stefano Teso

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#artificial intelligence Preprint Oct 2026

Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark

Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined ground truth, such as a motif planted in the data. This protocol implicitly assumes that a GNN trained on such data relies on the intended motif. Although prior work has quest...

Steve Azzolin, Francesco Paolo Nerini, Stefano Teso 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