提出统一方法，同时扰动节点特征和结构生成可解释的反事实解释。
COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations
- 联合优化节点特征与边结构扰动，生成最小有效修改。
- 在真实数据集上验证，显著提升解释的现实性与可读性。
- 支持连续与离散特征，适用于多种图神经网络模型。
反事实解释已成为揭示图神经网络（GNN）决策过程的重要工具。然而，现有方法主要关注边的修改，常忽略节点特征扰动对模型预测的关键影响。为此，我们提出COMBINEX，一种针对节点分类与图分类任务的新型GNN解释器。不同于以往将结构与特征扰动分开处理的方法，COMBINEX通过联合优化边与节点特征的修改，实现两者间的最优平衡。该统一策略确保在最小且合理的扰动下改变模型预测，生成更真实、可解释的反事实结果。此外，COMBINEX能无缝处理连续与离散节点特征，具备跨不同数据集和GNN架构的通用性。在多个真实世界数据集及多种GNN架构上的实验表明，该方法在解释效果与鲁棒性方面均优于现有基线。
原文摘要 · Abstract (English)
Counterfactual explanations have emerged as a powerful tool to unveil the opaque decision-making processes of graph neural networks (GNNs). However, existing techniques primarily focus on edge modifications, often overlooking the crucial role of node feature perturbations in shaping model predictions. To address this limitation, we propose COMBINEX, a novel GNN explainer that generates counterfactual explanations for both node and graph classification tasks. Unlike prior methods, which treat structural and feature-based changes independently, COMBINEX optimally balances modifications to edges and node features by jointly optimizing these perturbations. This unified approach ensures minimal yet effective changes required to flip a model's prediction, resulting in realistic and interpretable counterfactuals. Additionally, COMBINEX seamlessly handles both continuous and discrete node features, enhancing its versatility across diverse datasets and GNN architectures. Extensive experiments on real-world datasets and various GNN architectures demonstrate the effectiveness and robustness of our approach over existing baselines.
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