  Inspired by the recent success of self-supervised methods applied on images,
self-supervised learning on graph structured data has seen rapid growth
especially centered on augmentation-based contrastive methods. However, we
argue that without carefully designed augmentation techniques, augmentations on
graphs may behave arbitrarily in that the underlying semantics of graphs can
drastically change. As a consequence, the performance of existing
augmentation-based methods is highly dependent on the choice of augmentation
scheme, i.e., hyperparameters associated with augmentations. In this paper, we
propose a novel augmentation-free self-supervised learning framework for
graphs, named AFGRL. Specifically, we generate an alternative view of a graph
by discovering nodes that share the local structural information and the global
semantics with the graph. Extensive experiments towards various node-level
tasks, i.e., node classification, clustering, and similarity search on various
real-world datasets demonstrate the superiority of AFGRL. The source code for
AFGRL is available at https://github.com/Namkyeong/AFGRL.
