[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3837/tiis.2018.05.013

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)

Publication Information

KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.5, 2018 , pp. 2142-2161 More about this Journal

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.

Keywords

community detection; interaction model; complex network; Ego Network; Leader;

Citations & Related Records

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