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Dynamic Community Mining based on Behavior Prediction

Volume 14, Number 7, July 2018, pp. 1590-1599
DOI: 10.23940/ijpe.18.07.p23.15901599

Xiao Chena, Xinzhuan Hub, Xiao Panc, and Jingfeng Guod,e

aNetwork Technology Center, Hebei Normal University of Science and Technology, Qinhuangdao, 066004, China
bCollege of Economics and Management, YanShan University, Qinhuangdao, 066004, China
cCollege of Economic and Management, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China
dCollege of Information Science and Engineering, YanShan University, Qinhuangdao, 066004, China
eThe Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, 066004, China

(Submitted on March 29, 2018; Revised on May 5, 2018; Accepted on June 8, 2018)

Abstract:

Dynamic network research has been a new trend in recent years. Based on the influence of vertex behavior on community structure, this paper studies signed network dynamic community mining. Firstly, the set pair connection degree is introduced to describe the relation between vertices, and the edge prediction model of signed network is proposed by taking into account the variability of the relation between vertices. Secondly, based on the prediction model, a set pair signed networks dynamic model is proposed by adding time axis T to the signed network. Then, based on the dynamic model, the evolution of signed networks and community discovering are studied. Finally, network evolution law and community stability are analyzed by using the connection trend and connection entropy in set pair theory, and the accuracy and validity of the dynamic community mining algorithm are verified by experiments.

 

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