ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (11): 2611-2619.doi: 10.7544/issn1000-1239.2017.20160741

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Dynamic Social Network Community Detection Algorithm Based on Hidden Markov Model

Yi Peng1, Zhou Qiao1, Men Haosong2   

  1. 1(National Digital Switching System Engineering & Technology Research & Development Center, Zhengzhou 450001); 2(Department of Information, Ministry of Science & Technology of the People's Republic of China, Beijing 100000)
  • Online:2017-11-01

Abstract: With the continuous development of the Internet, most social networks have gradually demonstrated dynamic characteristics, and dynamic analysis of social network community has a very important significance on the understanding of the structure and function of social networks in real life. The HMM_DC algorithm (hidden Markov model based on dynamic community detection) is proposed according to the HMM (hidden Markov model) to detect the community in dynamic social network. Firstly, the algorithm transforms the community detection problem to get the optimal status chain in hidden Markov model considering the history information and characteristics in dynamic social networks. And the algorithm uses the observed chain and status chain to represent the community structure and node information, and can identify the community structure without extra information. Finally, this algorithm and three other algorithms are used to make comparable simulation experiments with VAST data set, ENRON data set and Facebook social network data set. Experimental results show that HMM_DC algorithm performs effectively and accurately in identifying the community structure in the dynamic social network and the value of Q and NMI can be raised greatly compared with other three algorithms.

Key words: dynamic social network, hidden Markov model (HMM), optimal status chain, community structure, community detection

CLC Number: