Detecting communities by asymmetric intimacy in directed-weighted network

Xingyuan Wang () and Xiaomeng Qin
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Xingyuan Wang: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Liaoning, Dalian, 116024, China
Xiaomeng Qin: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Liaoning, Dalian, 116024, China

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 01, 1-14

Abstract: Community detection and analysis have attracted wide public concerns over the recent years. Meanwhile, many related algorithms in complex networks have been proposed. However, most of them concentrate on undirected and unweighted networks. Concerning the significant theoretical value and potential application foreground for directed-weighted networks, in this paper, a novel hierarchical communities detection algorithm (termed as DCBAI) has been proposed on the basis of asymmetric intimacy between nodes. Community structures are effectively detected by node clustering algorithm in directed-weighted network, and a set of optimal communities are generated. In addition, a new and asymmetric parameter is adopted to measure the intimate relationship between nodes. We make some simulation using the proposed algorithm in real-world networks and artificial networks, and the result obtained proves that the parameter can describe the direct and indirect relationships between two nodes. Eventually, comparison with similar algorithms shows that our proposed algorithm has better performance.

Keywords: Complex networks; directed-weighted network; asymmetric intimacy (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S0129183117500061

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