Proposed as a solution to the inherent black-box limitations of graph neural networks (GNNs), post-hoc GNN explainers aim to provide precise and insightful explanations of the behaviours exhibited by trained GNNs. Despite their recent notable advancements in academic and industrial contexts, the robustness of post-hoc GNN explainers remains unexplored when confronted with label noise. To bridge this gap, we conduct a systematic empirical investigation to evaluate the efficacy of diverse post-hoc GNN explainers under varying degrees of label noise. Our results reveal several key insights: Firstly, post-hoc GNN explainers are susceptible to label perturbations. Secondly, even minor levels of label noise, inconsequential to GNN performance, harm the quality of generated explanations substantially. Lastly, we engage in a discourse regarding the progressive recovery of explanation effectiveness with escalating noise levels.

翻译：摘要：为应对图神经网络（GNN）固有的黑箱局限性而提出的后验GNN解释器，旨在精确且深入地解释训练后GNN所呈现的行为。尽管近期在学术与工业领域取得了显著进展，但面对标签噪声时后验GNN解释器的鲁棒性仍未被充分探究。为填补这一空白，我们开展了系统性实证研究，评估不同后验GNN解释器在不同程度标签噪声下的效能。研究结果揭示了若干关键洞见：其一，后验GNN解释器对标签扰动具有敏感性；其二，即使微小到不影响GNN性能的标签噪声，也会显著损害所生成解释的质量；最后，我们讨论了随噪声水平升高解释效能逐步恢复的现象。