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Information 2018, 9(9), 218; https://doi.org/10.3390/info9090218

Community Detection Based on Differential Evolution Using Modularity Density

1
College of Software, Dalian University of Foreign Languages, Dalian 116041, China
2
School of Computer Science, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Received: 25 June 2018 / Revised: 20 August 2018 / Accepted: 23 August 2018 / Published: 30 August 2018
(This article belongs to the Section Information Systems)
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Abstract

Currently, many community detection methods are proposed in the network science field. However, most contemporary methods only employ modularity to detect communities, which may not be adequate to represent the real community structure of networks for its resolution limit problem. In order to resolve this problem, we put forward a new community detection approach based on a differential evolution algorithm (CDDEA), taking into account modularity density as an optimized function. In the CDDEA, a new tuning parameter is used to recognize different communities. The experimental results on synthetic and real-world networks show that the proposed algorithm provides an effective method in discovering community structure in complex networks. View Full-Text
Keywords: complex networks; community detection; differential evolution; modularity density complex networks; community detection; differential evolution; modularity density
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Liu, C.; Liu, Q. Community Detection Based on Differential Evolution Using Modularity Density. Information 2018, 9, 218.

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