Web: http://arxiv.org/abs/2209.07699

Sept. 19, 2022, 1:11 a.m. | Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang, Yiyue Qian, Yanfang Ye, Chuxu Zhang

cs.LG updates on arXiv.org arxiv.org

Graph contrastive learning (GCL) is prevalent to tackle the supervision
shortage issue in graph learning tasks. Many recent GCL methods have been
proposed with various manually designed augmentation techniques, aiming to
implement challenging augmentations on the original graph to yield robust
representation. Although many of them achieve remarkable performances, existing
GCL methods still struggle to improve model robustness without risking losing
task-relevant information because they ignore the fact the augmentation-induced
latent factors could be highly entangled with the original graph, …

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