Recently developed graph contrastive learning (GCL) approaches compare two different "views" of the same graph in order to learn node/graph representations. The core assumption of these approaches is that by graph augmentation, it is possible to generate several structurally different but semantically similar graph structures, and therefore, the identity labels of the original and augmented graph/nodes should be identical. However, in this paper, we observe that this assumption does not always hold, for example, any perturbation to nodes or edges in a molecular graph will change the graph labels to some degree. Therefore, we believe that augmenting the graph structure should be accompanied by an adaptation of the labels used for the contrastive loss. Based on this idea, we propose ID-MixGCL, which allows for simultaneous modulation of both the input graph and the corresponding identity labels, with a controllable degree of change, leading to the capture of fine-grained representations from unlabeled graphs. Experimental results demonstrate that ID-MixGCL improves performance on graph classification and node classification tasks, as demonstrated by significant improvements on the Cora, IMDB-B, and IMDB-M datasets compared to state-of-the-art techniques, by 3-29% absolute points.

翻译：近期发展的图对比学习方法通过比较同一图的两个不同“视图”来学习节点/图表示。这些方法的核心假设是，通过图增强可以生成多个结构不同但语义相似的图结构，因此原始图/节点与增强图/节点的身份标签应保持一致。然而在本文中，我们观察到这一假设并不总是成立——例如，对分子图中节点或边的任何扰动都会在一定程度上改变图标签。基于这一认识，我们认为图结构增强应伴随对比损失所用标签的适应性调整。为此，我们提出ID-MixGCL方法，能够同步调节输入图及其对应的身份标签，且变化程度可控，从而从未标记图中捕获细粒度表示。实验结果表明，ID-MixGCL在Cora、IMDB-B和IMDB-M数据集上相较现有最优技术取得3-29%的绝对提升，显著改进了图分类和节点分类任务的性能。