On the Robustness of Post-hoc GNN Explainers to Label Noise

Zhiqiang Zhong, Yangqianzi Jiang, Davide Mottin

Keywords: Post-hoc Graph Neural Network Explainers, Robustness, Label Noise

TL;DR: This extended abstract represents an exploration into the robustness of post-hoc GNN explainers against label noise.

Abstract: 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.

Submission Type: Extended abstract (max 4 main pages).

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Submission Number: 31