Graph Neural Networks (GNNs) are sensitive to structural noise from adversarial attacks or imperfections. Existing graph contrastive learning (GCL) methods typically rely on either random perturbations (e.g., edge dropping) for diversity or spectral augmentations (e.g., SVD) to preserve structural priors. However, random perturbations are structure-agnostic and may remove critical edges, while SVD-based views often lack sufficient diversity. Integrating these paradigms is challenging as they operate on discrete edge removal and continuous matrix factorization, respectively.We propose SPGCL, a framework for robust GCL via SVD-guided structural perturbation. Leveraging a recently developed SVD-based method that generalizes structural perturbation theory to arbitrary graphs, we design a two-stage strategy: (1) lightweight stochastic edge removal to inject diversity, and (2) truncated SVD to derive a structure-aware scoring matrix for sparse top-$P$ edge recovery. This integration offers three advantages: (1) Robustness to accidental deletion, as important edges can be recovered by SVD-guided scoring; (2) Enrichment with missing links, creating more informative contrastive views by introducing semantically meaningful edges; and (3) Controllable structural discrepancy, ensuring contrastive signals stem from semantic differences rather than edge-number gaps.Furthermore, we incorporate a contrastive fusion module with a global similarity constraint to align embeddings. Extensive experiments on ten benchmark datasets demonstrate that SPGCL consistently improves the robustness and accuracy of GNNs, outperforming state-of-the-art GCL and structure learning methods, validating its effectiveness in integrating previously disparate paradigms.

翻译：图神经网络（GNNs）对来自对抗攻击或结构缺陷的噪声较为敏感。现有的图对比学习方法通常依赖随机扰动（如边丢弃）以增加多样性，或采用谱增强（如奇异值分解）以保持结构先验。然而，随机扰动与结构无关，可能移除关键边；而基于奇异值分解的视图往往缺乏足够的多样性。由于这两种范式分别基于离散的边移除和连续的矩阵分解，其整合具有挑战性。本文提出SPGCL，一种通过SVD引导的结构扰动实现鲁棒图对比学习的框架。借助一种新近发展的、将结构扰动理论推广至任意图的基于奇异值分解的方法，我们设计了一个两阶段策略：（1）轻量级随机边移除以注入多样性；（2）截断奇异值分解以生成结构感知的评分矩阵，用于稀疏的top-$P$边恢复。该整合具有三大优势：（1）对意外删除的鲁棒性，因为重要边可通过SVD引导的评分得以恢复；（2）通过引入缺失链接进行信息增强，从而创建更具信息量的对比视图；（3）可控的结构差异，确保对比信号源于语义差异而非边数差距。此外，我们引入了一个结合全局相似性约束的对比融合模块以对齐嵌入表示。在十个基准数据集上的大量实验表明，SPGCL持续提升了GNN的鲁棒性与准确性，其性能优于当前最先进的图对比学习与结构学习方法，验证了其在整合先前分离范式方面的有效性。