Augmentation-Free Self-Supervised Learning on Graphs

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2022

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v36i7.20700