Deep graph learning for semi-supervised classification

29 May 2020 Guangfeng Lin Xiaobing Kang Kaiyang Liao Fan Zhao Yajun Chen

Graph learning (GL) can dynamically capture the distribution structure (graph structure) of data based on graph convolutional networks (GCN), and the learning quality of the graph structure directly influences GCN for semi-supervised classification. Existing methods mostly combine the computational layer and the related losses into GCN for exploring the global graph(measuring graph structure from all data samples) or local graph (measuring graph structure from local data samples)... (read more)

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