ATEX-CF: 为图神经网络生成对抗感知的反事实解释
ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks
AI总结：
ATEX-CF通过整合对抗攻击与反事实解释，为图神经网络生成忠实且合理的实例级解释，提升模型可解释性。
摘要：
反事实解释为图神经网络（GNNs）提供了一种直观的解释方法，通过识别最小的变化来改变模型的预测，从而回答“什么必须不同以产生不同的结果？”。在本工作中，我们提出了一种新的框架ATEX-CF，该框架将对抗攻击技术与反事实解释生成相结合——这种联系得以实现的原因是它们共享的目标是翻转节点的预测，但扰动策略有所不同：对抗攻击通常依赖于边的添加，而反事实方法通常使用删除。与传统方法将解释和攻击分开处理不同，我们的方法高效地整合了边的添加和删除，基于理论，利用对抗性洞察来探索有影响力的反事实。此外，通过在受限扰动预算下联合优化保真度、稀疏性和合理性，我们的方法生成了实例级的解释，这些解释既信息丰富又合理。在合成和现实世界的节点分类基准上的实验表明，ATEX-CF生成的解释是忠实的、简洁的和合理的，突显了将对抗性洞察整合到GNNs的反事实推理中的有效性。
英文摘要：
Counterfactual explanations offer an intuitive way to interpret graph neural networks (GNNs) by identifying minimal changes that alter a model's prediction, thereby answering "what must differ for a different outcome?". In this work, we propose a novel framework, ATEX-CF that unifies adversarial attack techniques with counterfactual explanation generation-a connection made feasible by their shared goal of flipping a node's prediction, yet differing in perturbation strategy: adversarial attacks often rely on edge additions, while counterfactual methods typically use deletions. Unlike traditional approaches that treat explanation and attack separately, our method efficiently integrates both edge additions and deletions, grounded in theory, leveraging adversarial insights to explore impactful counterfactuals. In addition, by jointly optimizing fidelity, sparsity, and plausibility under a constrained perturbation budget, our method produces instance-level explanations that are both informative and realistic. Experiments on synthetic and real-world node classification benchmarks demonstrate that ATEX-CF generates faithful, concise, and plausible explanations, highlighting the effectiveness of integrating adversarial insights into counterfactual reasoning for GNNs.
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。
