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arXiv · 2112.02472

Augmentation-Free Self-Supervised Learning on Graphs

Namkyeong Lee·Junseok Lee·Chanyoung Park
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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, 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.

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Namkyeong Lee, Junseok Lee, Chanyoung Park. 2021-12-05. Augmentation-Free Self-Supervised Learning on Graphs. https://arxiv.org/abs/2112.02472

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