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arXiv · 2602.04360

Counterfactual Explanations for Hypergraph Neural Networks

Fabiano Veglianti·Lorenzo Antonelli·Gabriele Tolomei
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Abstract

Hypergraph neural networks (HGNNs) effectively model higher-order interactions in many real-world systems but remain difficult to interpret, limiting their deployment in high-stakes settings. We introduce CF-HyperGNNExplainer, a counterfactual explanation method for HGNNs that identifies the minimal structural changes required to alter a model's prediction. The method generates counterfactual hypergraphs using actionable edits limited to removing node-hyperedge incidences or deleting hyperedges, producing concise and structurally meaningful explanations. Extensive experiments on hypergraph benchmark datasets show that CF-HyperGNNExplainer generates valid and concise counterfactuals, highlighting the higher-order relations most critical to HGNN decisions.

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Fabiano Veglianti, Lorenzo Antonelli, Gabriele Tolomei. 2026-02-04. Counterfactual Explanations for Hypergraph Neural Networks. https://arxiv.org/abs/2602.04360

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