Augmentation-Free Self-Supervised Learning on Graphs

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 augmentationbased 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 AF- GRL. 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 realworld datasets demonstrate the superiority of AFGRL. The source code for AFGRL is available at https: //github. com/ Namkyeong/AFGRL.

Authors

• Namkyeong Lee KAIST
• Junseok Lee KAIST
• Chanyoung Park Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology

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