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

• URL: http://arxiv.org/abs/2606.05756v1
• Date: Thu, 04 Jun 2026 06:32:02 GMT
• Title: Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability
• Abstract summary: We propose HPME, a Hard-Perturbation Mixup Explanation framework grounded in a generalized Graph Information Bottleneck.<n>We show that HPME achieves state-of-the-art performance in generating robust and interpretable explanations across both synthetic and real-world datasets.
• Score: 23.41154199942488
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Robust Multimodal Recommendation via Graph Retrieval-Enhanced Modality Completion [71.20734649881258]
  Real-world multimodal datasets often suffer from modality incompleteness due to sensor failures, annotation scarcity, or privacy constraints.<n>One effective solution is modality completion, which reconstructs missing features to provide modality-complete graphs for downstream tasks.<n>We propose GRE-MC, a Graph Retrieval-Enhanced Modality Completion framework, to overcome these limitations.
  arXiv  Detail & Related papers  (2026-05-01T13:50:52Z)
• Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learning [10.65673380743972]
  We propose NSG (Node Splitting Graph)-MoE, a multimodal graph learning framework that integrates a node-splitting and graph-rewiring mechanism.<n>It explicitly decomposes each node into modality-specific components and assigns relation-aware experts to process heterogeneous message flows.<n>Experiments on three multimodal benchmarks demonstrate that NSG-MoE consistently surpasses strong baselines.
  arXiv  Detail & Related papers  (2026-01-20T13:38:50Z)
• RAW-Explainer: Post-hoc Explanations of Graph Neural Networks on Knowledge Graphs [0.0]
  RAW-Explainer is a novel framework designed to generate connected, concise, and thus interpretable subgraph explanations for link prediction.<n>Unlike existing methods tailored to knowledge graphs, our approach employs a neural network to parameterize the explanation generation process.<n>Our approach strikes a balance between explanation quality and computational efficiency.
  arXiv  Detail & Related papers  (2025-06-14T15:55:17Z)
• Generative Risk Minimization for Out-of-Distribution Generalization on Graphs [71.48583448654522]
  We propose an innovative framework, named Generative Risk Minimization (GRM), designed to generate an invariant subgraph for each input graph to be classified, instead of extraction.<n>We conduct extensive experiments across a variety of real-world graph datasets for both node-level and graph-level OOD generalization.
  arXiv  Detail & Related papers  (2025-02-11T21:24:13Z)
• Rewiring Techniques to Mitigate Oversquashing and Oversmoothing in GNNs: A Survey [0.0]
  Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their effectiveness is often constrained by two critical challenges. Oversquashing, where the excessive compression of information from distant nodes results in significant information loss, and oversmoothing, where repeated message-passing iterations homogenize node representations, obscuring meaningful distinctions. In this survey, we examine graph rewiring techniques, a class of methods designed to address these structural bottlenecks by modifying graph topology to enhance information diffusion.
  arXiv  Detail & Related papers  (2024-11-26T13:38:12Z)
• Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting [50.181824673039436]
  We propose a Graph Structure Self-Contrasting (GSSC) framework that learns graph structural information without message passing. The proposed framework is based purely on Multi-Layer Perceptrons (MLPs), where the structural information is only implicitly incorporated as prior knowledge. It first applies structural sparsification to remove potentially uninformative or noisy edges in the neighborhood, and then performs structural self-contrasting in the sparsified neighborhood to learn robust node representations.
  arXiv  Detail & Related papers  (2024-09-09T12:56:02Z)
• UGMAE: A Unified Framework for Graph Masked Autoencoders [67.75493040186859]
  We propose UGMAE, a unified framework for graph masked autoencoders. We first develop an adaptive feature mask generator to account for the unique significance of nodes. We then design a ranking-based structure reconstruction objective joint with feature reconstruction to capture holistic graph information.
  arXiv  Detail & Related papers  (2024-02-12T19:39:26Z)
• MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation [6.307753856507624]
  Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. Post-hoc instance-level explanation methods have been proposed to understand GNN predictions. We shed light on the existence of the distribution shifting issue in existing methods, which affects explanation quality.
  arXiv  Detail & Related papers  (2023-07-15T15:46:38Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Information Obfuscation of Graph Neural Networks [96.8421624921384]
  We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
  arXiv  Detail & Related papers  (2020-09-28T17:55:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.