On the Robustness of Post-hoc GNN Explainers to Label Noise
This addresses a critical gap in ensuring reliable explanations for GNNs in noisy real-world applications, though it is incremental as it focuses on evaluating existing methods under new conditions.
The study investigated the robustness of post-hoc GNN explainers to label noise, finding that these explainers are highly susceptible to even minor label perturbations, which substantially harm explanation quality without affecting GNN performance.
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.