While Graph Contrastive Learning (GCL) has attracted considerable attention in the field of graph self-supervised learning, its performance heavily relies on data augmentations that are expected to generate semantically consistent positive pairs. Existing strategies typically resort to random perturbations or local structure preservation, yet lack explicit control over global structural consistency between augmented views. To address this limitation, we propose Fractal Graph Contrastive Learning (FractalGCL), a theory-driven framework introducing two key innovations: a renormalisation-based augmentation that generates structurally aligned positive views via box coverings; and a fractal-dimension-aware contrastive loss that aligns graph embeddings according to their fractal dimensions, equipping the method with a fallback mechanism guaranteeing a performance lower bound even on non-fractal graphs. While combining the two innovations markedly boosts graph-representation quality, it also adds non-trivial computational overhead. To mitigate the computational overhead of fractal dimension estimation, we derive a one-shot estimator by proving that the dimension discrepancy between original and renormalised graphs converges weakly to a centred Gaussian distribution. This theoretical insight enables a reduction in dimension computation cost by an order of magnitude, cutting overall training time by approximately 61\%. The experiments show that FractalGCL not only delivers state-of-the-art results on standard benchmarks but also outperforms traditional and latest baselines on traffic networks by an average margin of about remarkably 4\%. Codes are available at (https://anonymous.4open.science/r/FractalGCL-0511/).

翻译：尽管图对比学习（GCL）在图自监督学习领域引起了广泛关注，但其性能严重依赖于能够生成语义一致正样本对的数据增强策略。现有方法通常采用随机扰动或局部结构保持，但缺乏对增强视图间全局结构一致性的显式控制。为克服这一局限性，我们提出分形图对比学习（FractalGCL），这是一个理论驱动的框架，引入两项关键创新：基于重归一化的增强方法，通过盒覆盖生成结构对齐的正样本视图；以及分形维度感知的对比损失函数，根据图的分形维度对齐图嵌入，使该方法具备回退机制，即使在非分形图上也能保证性能下限。虽然两项创新结合显著提升了图表示质量，但也带来了可观的计算开销。为缓解分形维度估计的计算负担，我们通过证明原始图与重归一化图间的维度差异弱收敛于中心高斯分布，推导出单次估计器。这一理论洞见将维度计算成本降低一个数量级，使总训练时间减少约61%。实验表明，FractalGCL不仅在标准基准测试中取得最先进的结果，还在交通网络数据集上以平均约4%的显著优势超越传统及最新基线方法。代码公开于（https://anonymous.4open.science/r/FractalGCL-0511/）。