Full-Text Search:
Home|About CNKI|User Service|中文
Add to Favorite Get Latest Update

Dual Supervised Network Embedding Based Community Detection Algorithm

ZHENG Wenping;WANG Yingnan;YANG Gui;School of Computer and Information Technology, Shanxi University;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University;Institute of Intelligent Information Processing, Shanxi University;  
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.
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©CNKI All Rights Reserved