通过多尺度不确定性建模，提升图神经网络的鲁棒性与可解释性。
Hierarchical Uncertainty-Aware Graph Neural Network
- 构建多尺度节点聚类与不确定性估计框架
- 在半监督分类任务中保持高精度且抗噪声扰动
- 适合关注模型可信度与鲁棒性的研究者
近期图神经网络研究探索了捕捉局部不确定性并利用图层次结构以缓解数据稀疏性、挖掘结构特性。然而，这两种方法的协同整合仍不充分。本文提出一种新架构——分层不确定性感知图神经网络（HU-GNN），将多尺度表征学习、严谨的不确定性估计和自监督嵌入多样性统一于一个端到端框架中。HU-GNN 自适应形成节点聚类，并在从单个节点到高层级的多个结构尺度上估计不确定性。这些不确定性估计指导稳健的消息传递机制与注意力加权，有效抑制噪声和对抗性扰动，同时在半监督分类任务中保持预测准确性。我们还提供了关键理论贡献，包括概率形式化、严格的不确定性校准保证及形式化的鲁棒性边界。在标准基准上的大量实验表明，该模型在鲁棒性和可解释性方面达到当前最优水平。
原文摘要 · Abstract (English)
Recent research on graph neural networks (GNNs) has explored mechanisms for capturing local uncertainty and exploiting graph hierarchies to mitigate data sparsity and leverage structural properties. However, the synergistic integration of these two approaches remains underexplored. This work introduces a novel architecture, the Hierarchical Uncertainty-Aware Graph Neural Network (HU-GNN), which unifies multi-scale representation learning, principled uncertainty estimation, and self-supervised embedding diversity within a single end-to-end framework. Specifically, HU-GNN adaptively forms node clusters and estimates uncertainty at multiple structural scales from individual nodes to higher levels. These uncertainty estimates guide a robust message-passing mechanism and attention weighting, effectively mitigating noise and adversarial perturbations while preserving predictive accuracy on semi-supervised classification tasks. We also offer key theoretical contributions, including a probabilistic formulation, rigorous uncertainty-calibration guarantees, and formal robustness bounds. Extensive experiments on standard benchmarks demonstrate that our model achieves state-of-the-art robustness and interpretability.
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