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Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method
March 22, 2024, 4:43 a.m. | Yulan Hu, Sheng Ouyang, Jingyu Liu, Ge Chen, Zhirui Yang, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Yong Liu
cs.LG updates on arXiv.org arxiv.org
Abstract: Graph contrastive learning (GCL) has emerged as a representative graph self-supervised method, achieving significant success. The currently prevalent optimization objective for GCL is InfoNCE. Typically, it employs augmentation techniques to obtain two views, where a node in one view acts as the anchor, the corresponding node in the other view serves as the positive sample, and all other nodes are regarded as negative samples. The goal is to minimize the distance between the anchor node …
abstract anchor arxiv augmentation cs.ai cs.lg graph node optimization ranking simple success type view
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