Community detection in complex networks by using membrane algorithm

Chuang Liu, Linan Fan (), Zhou Liu (), Xiang Dai (), Jiamei Xu () and Baoren Chang ()
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Chuang Liu: School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China
Linan Fan: School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China
Zhou Liu: School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China
Xiang Dai: School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China
Jiamei Xu: School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China
Baoren Chang: School of Information Engineering, Shenyang University, Shenyang, Liaoning 110000, P. R. China

International Journal of Modern Physics C (IJMPC), 2018, vol. 29, issue 01, 1-18

Abstract: Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

Keywords: Community detection; membrane algorithm; optimization; complex networks (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0129183118500031

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