  Contrastive learning (CL) has become the de-facto learning paradigm in
self-supervised learning on graphs, which generally follows the
"augmenting-contrasting" learning scheme. However, we observe that unlike CL in
computer vision domain, CL in graph domain performs decently even without
augmentation. We conduct a systematic analysis of this phenomenon and argue
that homophily, i.e., the principle that "like attracts like", plays a key role
in the success of graph CL. Inspired to leverage this property explicitly, we
propose HomoGCL, a model-agnostic framework to expand the positive set using
neighbor nodes with neighbor-specific significances. Theoretically, HomoGCL
introduces a stricter lower bound of the mutual information between raw node
features and node embeddings in augmented views. Furthermore, HomoGCL can be
combined with existing graph CL models in a plug-and-play way with light extra
computational overhead. Extensive experiments demonstrate that HomoGCL yields
multiple state-of-the-art results across six public datasets and consistently
brings notable performance improvements when applied to various graph CL
methods. Code is avilable at https://github.com/wenzhilics/HomoGCL.
