Graph Contrastive Learning (GCL) has shown superior performance in representation learning in graph-structured data. Despite their success, most existing GCL methods rely on prefabricated graph augmentation and homophily assumptions. Thus, they fail to generalize well to heterophilic graphs where connected nodes may have different class labels and dissimilar features. In this paper, we study the problem of conducting contrastive learning on homophilic and heterophilic graphs. We find that we can achieve promising performance simply by considering an asymmetric view of the neighboring nodes. The resulting simple algorithm, Asymmetric Contrastive Learning for Graphs (GraphACL), is easy to implement and does not rely on graph augmentations and homophily assumptions. We provide theoretical and empirical evidence that GraphACL can capture one-hop local neighborhood information and two-hop monophily similarity, which are both important for modeling heterophilic graphs. Experimental results show that the simple GraphACL significantly outperforms state-of-the-art graph contrastive learning and self-supervised learning methods on homophilic and heterophilic graphs. The code of GraphACL is available at https://github.com/tengxiao1/GraphACL.

翻译：图对比学习（Graph Contrastive Learning, GCL）在图结构数据的表示学习中展现了优越的性能。然而，现有的大多数GCL方法依赖于预定义的图增强与同质性假设，因而难以泛化至连边节点具有不同类别标签和相异特征的异配图。本文研究了在同配图与异配图上进行对比学习的问题。我们发现，仅通过考虑邻居节点的非对称视角即可实现显著性能。由此产生的简洁算法——图非对称对比学习（Asymmetric Contrastive Learning for Graphs, GraphACL），易于实现且不依赖图增强与同质性假设。我们提供理论及实验证据表明，GraphACL能够捕捉一跳局部邻域信息与两跳单亲相似性，这两者对异配图建模均至关重要。实验结果显示，简洁的GraphACL在同配图与异配图上均显著优于当前最先进的图对比学习及自监督学习方法。GraphACL代码已开源：https://github.com/tengxiao1/GraphACL。