Counterfactual Explanations for Hypergraph Neural Networks

• URL: http://arxiv.org/abs/2602.04360v1
• Date: Wed, 04 Feb 2026 09:34:03 GMT
• Title: Counterfactual Explanations for Hypergraph Neural Networks
• Authors: Fabiano Veglianti, Lorenzo Antonelli, Gabriele Tolomei,
• Abstract summary: Hypergraph neural networks (HGNNs) effectively model higher-order interactions in many real-world systems.<n>We introduce CF-HyperGNNExplainer, a counterfactual explanation method for HGNNs.
• Score: 2.342443373878122
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Experiments on three 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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