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.

翻译：超图神经网络（HGNNs）能够有效模拟众多现实系统中的高阶交互作用，但其可解释性不足限制了其在高风险场景中的应用。我们提出CF-HyperGNNExplainer方法，这是一种面向HGNN的反事实解释方法，能够识别改变模型预测所需的最小结构更改。该方法通过限制在移除节点-超边关联或删除超边的可操作编辑来生成反事实超图，从而产生简洁且结构清晰的解释。在超图基准数据集上的大量实验表明，CF-HyperGNNExplainer能够生成有效且简洁的反事实，凸显出对HGNN决策最关键的高阶关系。