ARXIV:2606.05756 · UNCATEGORIZED · SUBMITTED 06 JUN · 03:21 UTC · FRESHNESS FRESH

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Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability

Jialiang Yin · Zheng Zhao · Linsey Pang · Bo Dong · Bin Shi · Jiaxing Zhang · arXiv

ScienceToStartup currently rates this 0.0/10 on the public viability pass. Existing post-hoc explanation methods aim to improve explainability by identifying subgraphs that influence GNN predictions and adopt mixup strategies to…

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PROBLEM

Graph Neural Networks (GNNs) have demonstrated remarkable performance across a range of applications involving graph-structured data, particularly in high-stakes domains.

METHOD

Full abstract

Graph Neural Networks (GNNs) have demonstrated remarkable performance across a range of applications involving graph-structured data, particularly in high-stakes domains. However, the opaque nature of their decision-making processes limits their trustworthiness and broader adoption. Existing post-hoc explanation methods aim to improve explainability by identifying subgraphs that influence GNN predictions and adopt mixup strategies to alleviate the out-of-distribution (OOD) issue caused by using subgraphs for prediction. Yet, these approaches typically rely on soft masks, which are inherently unable to fully eliminate label-irrelevant information, allowing redundant structures to leak into the mixup process and hindering the resolution of the OOD problem, thereby degrading explanation fidelity. In this work, we propose HPME, a Hard-Perturbation Mixup Explanation framework grounded in a generalized Graph Information Bottleneck, which leverages graph pooling to extract discrete explanatory subgraphs and to yield an information-capacity bound to thoroughly compress label-irrelevant components. Furthermore, we introduce a novel mixup strategy built upon structure-level replacement, generating in-distribution explanations to effectively mitigate the distribution shift. Extensive experiments on diverse tasks demonstrate that HPME achieves state-of-the-art performance in generating robust and interpretable explanations across both synthetic and real-world datasets.

RESULT

WHY NOW

Uncategorized moved forward this cycle; last verified June 2026. Public score 0.0/10. Production flags indicate code availability.

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Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability

Jialiang Yin · Zheng Zhao · Linsey Pang · Bo Dong · Bin Shi · Jiaxing Zhang · arXiv

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Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability

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