COMBINEX：通过节点特征与结构扰动实现图神经网络统一反事实解释器
COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations
AI总结：
本文提出 COMBINEX，通过联合优化边结构与节点特征扰动，为节点和图分类生成最小、有效且可解释的 GNN 反事实解释。
摘要：
反事实解释已成为揭示图神经网络（GNN）不透明决策过程的有力工具。然而，现有技术主要关注边修改，往往忽视节点特征扰动在塑造模型预测中的关键作用。为解决这一局限，我们提出 COMBINEX，这是一种新颖的 GNN 解释器，可为节点分类和图分类任务生成反事实解释。与以往将结构变化和基于特征的变化独立处理的方法不同，COMBINEX 通过联合优化这些扰动，在边和节点特征的修改之间实现最优平衡。这种统一方法确保以最小且有效的变化翻转模型预测，从而产生真实且可解释的反事实。此外，COMBINEX 可无缝处理连续和离散节点特征，增强了其在不同数据集和 GNN 架构上的通用性。在真实世界数据集及多种 GNN 架构上的大量实验证明，本方法相比现有基线具有有效性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
