KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Enhanced Distance Dynamics Model for Community Detection via Ego-Leader

  • Cai, LiJun (College of Information Science and Engineering, Hunan University) ;
  • Zhang, Jing (College of Electrical and Information Engineering, Hunan University) ;
  • Chen, Lei (College of Electrical and Information Engineering, Hunan University) ;
  • He, TingQin (College of Information Science and Engineering, Hunan University)
  • Received : 2017.06.26
  • Accepted : 2017.11.16
  • Published : 2018.05.31
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Abstract

Distance dynamics model is an excellent model for uncovering the community structure of a complex network. However, the model has poor robustness. To improve the robustness, we design an enhanced distance dynamics model based on Ego-Leader and propose a corresponding community detection algorithm, called E-Attractor. The main contributions of E-Attractor are as follows. First, to get rid of sensitive parameter ${\lambda}$, Ego-Leader is introduced into the distance dynamics model to determine the influence of an exclusive neighbor on the distance. Second, based on top-k Ego-Leader, we design an enhanced distance dynamics model. In contrast to the traditional model, enhanced model has better robustness for all networks. Extensive experiments show that E-Attractor has good performance relative to several state-of-the-art algorithms.

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