为超图神经网络设计可解释性工具，找出最小改动让预测翻转。
Counterfactual Explanations for Hypergraph Neural Networks
- 通过删减节点-超边关联或删除超边生成反事实结构
- 在多个基准数据集上验证了解释的准确性和简洁性
- 适合需要理解高阶关系决策过程的研究者与工程师
超图神经网络（HGNNs）能有效建模真实世界系统中的高阶交互关系，但其可解释性差，限制了在高风险场景的应用。本文提出CF-HyperGNNExplainer，一种针对HGNN的反事实解释方法，旨在识别使模型预测发生变化所需的最小结构改动。该方法通过仅允许移除节点-超边关联或删除超边，生成具有行动意义的反事实超图，从而提供简洁且结构合理的解释。在多个超图基准数据集上的大量实验表明，CF-HyperGNNExplainer能够生成有效且紧凑的反事实样本，突出显示对HGNN决策最关键的一阶高阶关系。
原文摘要 · Abstract (English)
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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