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Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning

arXiv 2104.06317 ↗First posted 13 Apr 2021cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 13 Apr 2021.

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References · 26

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14 more
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