Graph-level contrastive learning, aiming to learn the representations for each graph by contrasting two augmented graphs, has attracted considerable attention. Previous studies usually simply assume that a graph and its augmented graph as a positive pair, otherwise as a negative pair. However, it is well known that graph structure is always complex and multi-scale, which gives rise to a fundamental question: after graph augmentation, will the previous assumption still hold in reality? By an experimental analysis, we discover the semantic information of an augmented graph structure may be not consistent as original graph structure, and whether two augmented graphs are positive or negative pairs is highly related with the multi-scale structures. Based on this finding, we propose a multi-scale subgraph contrastive learning architecture which is able to characterize the fine-grained semantic information. Specifically, we generate global and local views at different scales based on subgraph sampling, and construct multiple contrastive relationships according to their semantic associations to provide richer self-supervised signals. Extensive experiments and parametric analyzes on eight graph classification real-world datasets well demonstrate the effectiveness of the proposed method.

翻译：图级对比学习旨在通过对比两个增强图来学习每个图的表示，近年来引起了广泛关注。先前的研究通常简单假设一个图与其增强图形成正样本对，否则为负样本对。然而，众所周知图结构总是复杂且多尺度的，这引出了一个根本性问题：在图增强后，先前的假设在现实中是否依然成立？通过实验分析，我们发现增强图结构的语义信息可能与原始图结构不一致，且两个增强图是正样本对还是负样本对与多尺度结构高度相关。基于这一发现，我们提出了一种多尺度子图对比学习架构，能够刻画细粒度的语义信息。具体而言，我们基于子图采样在不同尺度上生成全局和局部视图，并根据其语义关联构建多个对比关系，以提供更丰富的自监督信号。在八个图分类真实数据集上的大量实验和参数分析充分证明了所提方法的有效性。