识别导致图学习失效的不稳定的节点并加以隔离。
Spectrally unstable nodes drive reliability failures in graph learning
- 基于谱畸变分析定位对噪声敏感的关键节点。
- 通过隔离不稳定节点，提升多种图模型在攻击下的可靠性。
- 适合关注图神经网络鲁棒性的研究者和工程师。
图学习算法在图结构遭受对抗性扰动、固有噪声或由不完整观测构建时可能失效。本文发现，某些节点对对抗扰动和内在噪声的影响远大于其他节点。基于图谱畸变分析，我们识别出这些引发失败的关键节点，并提出一种可靠性感知的干预策略：将它们从主学习流程中隔离，仅在稳定子图上应用目标算法，再通过拓扑或质心传播恢复被隔离节点的预测结果。该方法在针对图神经网络的定向与非定向结构攻击、谱超图聚类及多视角谱聚类中均显著提升了可靠性。结果表明，节点级谱不稳定性是理解并缓解图学习可靠性问题的共性机制。
原文摘要 · Abstract (English)
Graph-learning algorithms can fail when graph structure is adversarially perturbed, intrinsically noisy or constructed from imperfect observations. Here we show that some nodes bear much greater responsibility than others for allowing adversarial perturbations and intrinsic noise to harm graph-learning algorithms. Building on graph-spectral distortion analysis, we identify these failure-driving nodes and introduce a reliability-aware intervention that isolates them from the main learning step. The target algorithm is applied to a stable induced subgraph, and predictions for isolated nodes are recovered through topology- or centroid-based propagation. Across graph neural networks under targeted and non-targeted structural attacks, spectral hypergraph clustering and multi-view spectral clustering, this principle improves reliability under both adversarial and intrinsic noise. These results suggest that node-level spectral instability provides a common mechanism for understanding and mitigating reliability failures in graph learning.
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