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.
Original language | English |
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Publication date | Dec 2022 |
Publication status | Published - Dec 2022 |
Event | Learning on Graphs Conference 2022 - Virtual Duration: 9 Dec 2022 → 12 Dec 2022 |
Conference
Conference | Learning on Graphs Conference 2022 |
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Location | Virtual |
Period | 09/12/2022 → 12/12/2022 |