Dual Supervised Network Embedding Based Community Detection Algorithm

被引：0

作者：

Zheng W. ^{[1
,2
,3
]}

Wang Y. ^{[1
]}

Yang G. ^{[1
]}

机构：

[1] School of Computer and Information Technology, Shanxi University, Taiyuan

[2] Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan

[3] Institute of Intelligent Information Processing, Shanxi University, Taiyuan

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2022年 / 35卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Community Detection; Graph Auto-Encoder; Modularity; Network Embedding;

D O I：

10.16451/j.cnki.issn1003-6059.202203008

中图分类号：

学科分类号：

摘要：

A network embedding based community detection algorithm is easy to fall into local extremes during the independent node embedding or clustering process. Aiming at this problem, a dual supervised network embedding based community detection algorithm(DSNE) is proposed. Firstly, a graph auto-encoder is utilized to gain the embedding of nodes to maintain the first-order similarity of the network. Then, the modularity is optimized to find the communities with nodes tightly connected. The communities with similar nodes in the embedding space are discovered by self-supervised clustering optimization. A mutual supervision mechanism is introduced into DSNE to keep the consistency between the discovered communities in modularity optimization and self-supervised clustering and prevent the algorithm from falling into local extremes. Results of comparative experiments show DSNE exhibits better performance on 4 real complex networks. © 2022, Science Press. All right reserved.

引用

页码：283 / 290

页数：7

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