  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.
