Author

A. Panisson

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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

Inductive Correlation Clustering with Graph Neural Networks

This work uses Graph Neural Networks (GNNs) to solve Inductive Correlation Clustering, a novel generalization of the CC problem designed to handle unseen graph instances, and indicates that the method serves as an efficient pooling layer, enhancing the ability of GNNs to capture hierarchical structural information in n...

Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan et al. · 0 citations