SPGCL: Simple yet Powerful Graph Contrastive Learning via SVD-Guided Structural Perturbation

2026-01-20

AI Summary

This work addresses the sensitivity of graph neural networks (GNNs) to structural noise and the challenge faced by existing graph contrastive learning methods in simultaneously preserving structural priors and ensuring perturbation diversity. To this end, we propose SPGCL, a novel framework that uniquely integrates discrete stochastic edge dropping with continuous truncated singular value decomposition (SVD) to construct structure-aware contrastive views. Specifically, lightweight edge dropping introduces diversity, while SVD recovers critical edges and supplements semantically missing connections. A contrastive fusion module, incorporating a sparse Top-P recovery mechanism and global similarity constraints, enables controlled and robust structural perturbations. Extensive experiments on ten benchmark datasets demonstrate that SPGCL significantly improves both accuracy and noise robustness of GNNs, outperforming state-of-the-art graph contrastive learning and structure learning approaches.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• Graph Neural Networks
• Structural Noise
• Graph Contrastive Learning
• Data Augmentation
• Robustness

Innovation

• Graph Contrastive Learning
• SVD-Guided Perturbation
• Structural Robustness
• Edge Recovery
• Contrastive Fusion