Web: http://arxiv.org/abs/2209.06560

Sept. 16, 2022, 1:13 a.m. | Xin Zhang, Qiaoyu Tan, Xiao Huang, Bo Li

cs.LG updates on arXiv.org arxiv.org

Graph contrastive learning (GCL) has emerged as an effective tool for
learning unsupervised representations of graphs. The key idea is to maximize
the agreement between two augmented views of each graph via data augmentation.
Existing GCL models mainly focus on applying \textit{identical augmentation
strategies} for all graphs within a given scenario. However, real-world graphs
are often not monomorphic but abstractions of diverse natures. Even within the
same scenario (e.g., macromolecules and online communities), different graphs
might need diverse augmentations to …

arxiv augmentation graph personalized

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