Feb. 13, 2024, 5:42 a.m. | Liang Wang Xiang Tao Qiang Liu Shu Wu Liang Wang

cs.LG updates on arXiv.org arxiv.org

Self-supervised learning on graphs can be bifurcated into contrastive and generative methods. Contrastive methods, also known as graph contrastive learning (GCL), have dominated graph self-supervised learning in the past few years, but the recent advent of graph masked autoencoder (GraphMAE) rekindles the momentum behind generative methods. Despite the empirical success of GraphMAE, there is still a dearth of theoretical understanding regarding its efficacy. Moreover, while both generative and contrastive methods have been shown to be effective, their connections and differences …

alignment autoencoder autoencoders cs.lg generative graph graphs masked autoencoder self-supervised learning success supervised learning through

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