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.

翻译：超图神经网络（HGNNs）能够有效建模众多现实系统中的高阶交互，但其可解释性仍然不足，这限制了其在高风险场景中的部署。本文提出CF-HyperGNNExplainer，一种针对HGNNs的因果解释方法，该方法通过识别改变模型预测所需的最小结构变化来生成解释。该方法通过可操作的编辑操作（仅限于移除节点-超边关联或删除超边）生成因果超图，从而产生简洁且具有结构意义的解释。在三个基准数据集上的实验表明，CF-HyperGNNExplainer能够生成有效且简洁的因果解释，突显了对HGNN决策最为关键的高阶关系。