Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy for learning representations of diverse graphs including social and biomedical networks. GCL widely uses stochastic graph topology augmentation, such as uniform node dropping, to generate augmented graphs. However, such stochastic augmentations may severely damage the intrinsic properties of a graph and deteriorate the following representation learning process. We argue that incorporating an awareness of cohesive subgraphs during the graph augmentation and learning processes has the potential to enhance GCL performance. To this end, we propose a novel unified framework called CTAug, to seamlessly integrate cohesion awareness into various existing GCL mechanisms. In particular, CTAug comprises two specialized modules: topology augmentation enhancement and graph learning enhancement. The former module generates augmented graphs that carefully preserve cohesion properties, while the latter module bolsters the graph encoder's ability to discern subgraph patterns. Theoretical analysis shows that CTAug can strictly improve existing GCL mechanisms. Empirical experiments verify that CTAug can achieve state-of-the-art performance for graph representation learning, especially for graphs with high degrees. The code is available at https://doi.org/10.5281/zenodo.10594093, or https://github.com/wuyucheng2002/CTAug.

翻译：图对比学习已成为学习社交网络和生物医学网络等多种图结构表示的前沿策略。该方法广泛采用随机图拓扑增强（如均匀节点丢弃）生成增强图。然而，此类随机增强可能严重破坏图的固有属性，进而降低后续表示学习过程的性能。我们认为，在图增强与学习过程中融入对内聚子图的感知，有望提升图对比学习性能。为此，我们提出名为CTAug的新型统一框架，将内聚性意识无缝嵌入各类现有图对比学习机制中。具体而言，CTAug包含两个专用模块：拓扑增强优化模块与图学习强化模块。前者生成能谨慎保留内聚性质的增强图，后者则增强图编码器对子图模式的识别能力。理论分析表明，CTAug能严格改进现有图对比学习机制。实验验证显示，CTAug在图表示学习任务中达到当前最优性能，尤其适用于高密度图结构。代码可通过https://doi.org/10.5281/zenodo.10594093或https://github.com/wuyucheng2002/CTAug获取。