Augmentation-free self-supervised learning on graphs

Abstract

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, these methods pay little attention to the inherent distinction between images and graphs: while augmentation is well defined on images, it may behave arbitrarily on graphs. That is, 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 method for graphs, named AFGRL, which learns graph representations without using any arbitrary augmentation scheme. Specifically, we generate an alternative view of a graph by discovering nodes that share the local structural information and the global semantics within the graph. Extensive experiments toward various node-level tasks (i.e., node classification, clustering, and similarity search) and graph-level tasks (i.e. graph classification) on various real-world datasets demonstrate the superiority of AFGRL.