Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (1): 60-73.doi: 10.23940/ijpe.21.01.p6.6073

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Event-based Community Detection in Micro-Blog Networks

Hailu Yanga,b, Ying Zhanga*, Jin Zhangc, Deyun Chena,b, and Guanglu Suna   

  1. aSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150001, China
    bPostdoctoral Research Station of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150001, China
    cCollege of Automatic Control Engineering, Harbin Institute of of Petroleum, Harbin, 150028, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * Corresponding author. E-mail address:
  • Supported by:
    the National Natural Science Foundation of China (No61402126), Nature Science Foundation of Heilongjiang Province of China (NoF2016024), Heilongjiang Postdoctoral Science Foundation (NoLBH-Z15095), University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (NoUNPYSCT-2017094), Heilongjiang Province Foundation for Returned Scholars (NoLC2018030), Natural Science Foundation of Ningbo (No2019A610093), and National Training Programs of Innovation and Entrepreneurship for Undergraduates (No201810214020)


With the rapid development of mobile Internet, micro-blogs have been already integrated into people's work and family life. Traditional topic-based semantic community detection methods have not considered the sentiment tendency of users towards specific topics and therefore increase the probability of community fragmentation. To solve this problem, we propose an event-based community detection framework in the micro-blog network. Firstly, the major events in micro-blog within a sliding window are extracted. Then, the user's sentiment polarity of each event is used as the initial community label of each individual. Finally, the community detection procedure can be implemented by iteratively updating the community label. The simulation results indicate that the proposed method can capture the consistency of views of members within the same community, which brings higher accuracy and sentiment cohesion.

Key words: micro-blog networks, community detection, event extraction, sentiment tendency, label propagation