Title: A survey on network community detection based on evolutionary computation

Authors: Qing Cai; Lijia Ma; Maoguo Gong; Dayong Tian

Addresses: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province 710071, China ' Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province 710071, China ' Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province 710071, China ' Center for Quantum Computation and Intelligent Systems, University of Technology, Sydney, Broadway, NSW 2007, Australia

Abstract: Uncovering community structures of a complex network can help us to understand how the network functions. Over the past few decades, network community detection has attracted growing research interest from many fields. Many community detection methods have been developed. Network community structure detection can be modelled as optimisation problems. Due to their inherent complexity, these problems often cannot be well solved by traditional optimisation methods. For this reason, evolutionary algorithms have been adopted as a major tool for dealing with community detection problems. This paper presents a survey on evolutionary algorithms for network community detection. The evolutionary algorithms in this survey cover both single objective and multiobjective optimisations. The network models involve weighted/unweighted, signed/unsigned, overlapping/non-overlapping and static/dynamic ones.

Keywords: complex networks; community structures; community detection; evolutionary computation; multiobjective optimisation; network communities; modelling.

DOI: 10.1504/IJBIC.2016.076329

International Journal of Bio-Inspired Computation, 2016 Vol.8 No.2, pp.84 - 98

Received: 13 Nov 2014
Accepted: 18 Nov 2014
Published online: 04 May 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article