ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks
This work addresses the need for better interpretability in GNNs, particularly for node classification tasks, by integrating adversarial insights, though it is incremental in combining existing techniques.
The paper tackles the problem of interpreting graph neural networks (GNNs) by proposing ATEX-CF, a framework that unifies adversarial attack techniques with counterfactual explanation generation, resulting in faithful, concise, and plausible explanations as demonstrated on synthetic and real-world benchmarks.
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.