通过高频扰动增强图自监督学习，提升模型泛化能力。
Frequency-Corrupt Based Graph Self-Supervised Learning
- 基于低频贡献扰动节点边，生成偏向高频信息的损坏图。
- 在14个数据集上显著提升节点分类与图预测性能。
- 适合需要强鲁棒性的推荐系统和社交网络应用。
图自监督学习可减少对标注图数据的依赖，广泛应用于推荐、社交网络等场景。但现有方法常低估高频信号，易过拟合特定局部模式，限制表示质量与泛化能力。我们提出频率扰动图自监督学习（FC-GSSL），通过根据节点和边的低频贡献进行扰动，构建偏向高频信息的损坏图作为编码器输入；同时以低频和通用特征作为重建目标，迫使模型融合多频段信息。进一步设计多种采样策略，从采样结果的交集与并集中生成多样化的损坏图。通过对齐这些视图中的节点表示，模型能发现有效频率组合，降低对特定高频成分的依赖，提升鲁棒性。在14个涵盖节点分类、图预测与迁移学习的数据集上的实验表明，FC-GSSL持续提升性能与泛化能力。
原文摘要 · Abstract (English)
Graph self-supervised learning can reduce the need for labeled graph data and has been widely used in recommendation, social networks, and other web applications. However, existing methods often underuse high-frequency signals and may overfit to specific local patterns, which limits representation quality and generalization. We propose Frequency-Corrupt Based Graph Self-Supervised Learning (FC-GSSL), a method that builds corrupted graphs biased toward high-frequency information by corrupting nodes and edges according to their low-frequency contributions. These corrupted graphs are used as inputs to an autoencoder, while low-frequency and general features are reconstructed as supervision targets, forcing the model to fuse information from multiple frequency bands. We further design multiple sampling strategies and generate diverse corrupted graphs from the intersections and unions of the sampling results. By aligning node representations from these views, the model can discover useful frequency combinations, reduce reliance on specific high-frequency components, and improve robustness. Experiments on 14 datasets across node classification, graph prediction, and transfer learning show that FC-GSSL consistently improves performance and generalization.
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