Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (10): 121-125.doi: 10.11896/jsjkx.191000099

• Database & Big Data & Data Science • Previous Articles     Next Articles

Community Detection Algorithm Combing Community Embedding and Node Embedding

ZHAO Xia1, LI Xian1, ZHANG Ze-hua1, ZHANG Chen-wei2   

  1. 1 College of Information and Computer,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
    2 School of Computer Science,University of Illinois at Chicago,Chicago 60607,USA
  • Received:2019-10-15 Revised:2019-12-16 Online:2020-10-15 Published:2020-10-16
  • About author:ZHAO Xia,born in 1994,postgraduate.Her main research interests includeuncertainty theory and social network applications.
    ZHANG Ze-hua,born in 1981,Ph.D,master supervisor,is a member of China Computer Federation.His main research interests include granular computing,uncertain reasoning and know-ledge discovery on graph.
  • Supported by:
    ational Nature Science Foundation of China (61503273,61702356) and Youth Innovation Team Project of Taiyuan University of Technology (2014TD056)

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Abstract: As an important property of social networks,community plays an important role in understanding network functions and predicting evolution.It is a research hotspot in recent years to transform network nodes into low-dimensional dense feature vectors through network embedding and apply them to machine learning tasks such as community detection.The traditional network embedding method only focuses on node embedding and ignores the importance of community embedding.Aiming at such a problem,CNE,a method combining Community embedding and improved Node Embedding,is proposed to obtain node representation combining structure information and attribute information.Node embedding represents nodes as low-dimensional vectors.Similarly,community embedding represents communities as Gaussian distributions in low-dimensional spaces.They combine multiple node similarities to promote more accurate community detection results.The experimental results show that,compared with the traditional community detection algorithm and network embedding method on public datasets,the proposed CNE method has higher precision.

Key words: Community detection, Community embedding, Network embedding, Social network

CLC Number: 

  • TP181
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