Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning

• URL: http://arxiv.org/abs/2104.06317v1
• Date: Tue, 13 Apr 2021 15:53:48 GMT
• Title: Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning
• Authors: Shiyi Chen, Ziao Wang, Xinni Zhang, Xiaofeng Zhang, Dan Peng
• Abstract summary: Graph representation learning has long been an important yet challenging task for various real-world applications. Inspired by recent advances in unsupervised contrastive learning, this paper is motivated to investigate how the node-wise contrastive learning could be performed.
• Score: 4.909151538536424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph representation learning has long been an important yet challenging task for various real-world applications. However, their downstream tasks are mainly performed in the settings of supervised or semi-supervised learning. Inspired by recent advances in unsupervised contrastive learning, this paper is thus motivated to investigate how the node-wise contrastive learning could be performed. Particularly, we respectively resolve the class collision issue and the imbalanced negative data distribution issue. Extensive experiments are performed on three real-world datasets and the proposed approach achieves the SOTA model performance.

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