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.
@article{arxiv.2602.04360,
title = {Counterfactual Explanations for Hypergraph Neural Networks},
author = {Fabiano Veglianti and Lorenzo Antonelli and Gabriele Tolomei},
journal= {arXiv preprint arXiv:2602.04360},
year = {2026}
}