Deepwalk-aware graph convolutional networks

被引:21
作者
Jin, Taisong [1 ]
Dai, Huaqiang [2 ]
Cao, Liujuan [1 ]
Zhang, Baochang [3 ]
Huang, Feiyue [4 ]
Gao, Yue [5 ]
Ji, Rongrong [2 ]
机构
[1] Xiamen Univ, Sch Informat, Dept Comp Sci & Technol, Media Analyt & Comp Lab, Xiamen 361005, Peoples R China
[2] Xiamen Univ, Sch Informat, Dept Artificial Intelligence, Media Analyt & Comp Lab, Xiamen 361005, Peoples R China
[3] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100083, Peoples R China
[4] Tencent Youtu Lab, Shanghai 200233, Peoples R China
[5] Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
graph; convolutional networks; global information; fusion; node classification; NEURAL-NETWORKS;
D O I
10.1007/s11432-020-3318-5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Graph convolutional networks (GCNs) provide a promising way to extract the useful information from graph-structured data. Most of the existing GCNs methods usually focus on local neighborhood information based on specific convolution operations, and ignore the global structure of the input data. To extract the latent representation for the graph-structured data more effectively, we introduce a deepwalk strategy into GCNs to efficiently explore the global graph information. This strategy can complement the local neighborhood information of a graph, resulting in the more robust representation for the graph data. The fusion of the local neighboring and global structured information of a graph can further facilitate deep feature learning at the output layer of GCNs for node classification. Experimental results show that the proposed model has achieved state-of-the-art results on three benchmark datasets including Cora, Citeseer, and Pubmed citation networks.
引用
收藏
页数:15
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