eCite Digital Repository

A multi-objective adaptive evolutionary algorithm to extract communities in networks


Li, Q and Cao, Z and Ding, W and Li, Q, A multi-objective adaptive evolutionary algorithm to extract communities in networks, Swarm and Evolutionary Computation, 52 Article 100629. ISSN 2210-6502 (2020) [Refereed Article]


Copyright Statement

2019 Elsevier B.V. All rights reserved.

DOI: doi:10.1016/j.swevo.2019.100629


Community structure is one of the most important attributes of complex networks, which reveals the hidden rules and behavior characteristics of complex networks. Existing works need to pre-set weight parameters to control the different emphasis on the objective function, and cannot automatically identify the number of communities. In the process of optimization, there will be some challenges, such as premature and inefficiency. This paper presents a multi-objective adaptive fast evolutionary algorithm (F-SGCD) for community detection in complex networks. Firstly, it transforms the problem of community detection into a multi-objective optimization problem and constructs two objective functions of community score and community fitness. Secondly, an external elite gene pool is introduced to store non-inferior solutions with high fitness. At the same time, an adaptive genetic operator is executed to return a set of non-dominant solutions compromised between the two objective functions. Finally, a Pareto optimal solution with the highest modularity is selected and decoded to generate a set of independent subnetworks. Experiments show that the multi-objective adaptive fast evolutionary algorithm greatly improves the accuracy of community detection in complex networks, and can discover the hierarchical structure of complex networks better.

Item Details

Item Type:Refereed Article
Keywords:community detection, genetic algorithm, multi-objective, complex networks, adaptive
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:136633
Year Published:2020
Web of Science® Times Cited:18
Deposited By:Information and Communication Technology
Deposited On:2020-01-11
Last Modified:2020-04-30
Downloads:7 View Download Statistics

Repository Staff Only: item control page