Web: http://arxiv.org/abs/2206.02796

June 17, 2022, 1:11 a.m. | Xihong Yang, Yue Liu, Sihang Zhou, Xinwang Liu, En Zhu

cs.LG updates on arXiv.org arxiv.org

Graph Neural Networks (GNNs) have achieved promising performance in
semi-supervised node classification in recent years. However, the problem of
insufficient supervision, together with representation collapse, largely limits
the performance of the GNNs in this field. To alleviate the collapse of node
representations in semi-supervised scenario, we propose a novel graph
contrastive learning method, termed Mixed Graph Contrastive Network (MGCN). In
our method, we improve the discriminative capability of the latent feature by
enlarging the margin of decision boundaries and improving …

arxiv classification graph lg mixed network node semi-supervised

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