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：图对比学习在图结构数据的表示学习中展现了优越性能。尽管现有大多数图对比学习方法取得了成功，但它们依赖于预制图增强和同质性假设，因此在连接节点可能具有不同类别标签和不同特征的异配图上难以泛化。本文研究了在同配图和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图就能取得令人瞩目的性能。由此产生的简单算法——图非对称对比学习（GraphACL）——易于实现，且不依赖于图增强和同质性假设。我们通过理论和实验证明，GraphACL能够捕获单跳局部邻域信息和双跳同质性相似性，这两者对建模异配图均至关重要。实验结果表明，简单的GraphACL在同配图和异配图上均显著优于最先进的图对比学习和自监督学习方法。GraphACL的代码可在https://github.com/tengxiao1/GraphACL获取。