A new multiobjective evolutionary algorithm for community detection in dynamic complex networks
type: Item
instance of: scholarly article
title: A new multiobjective evolutionary algorithm for community detection in dynamic complex networks
zbMATH Open document ID: 1296.90143
author: Guoqiang Chen
author: Yuping Wang
author: Jingxuan Wei
published in: Mathematical Problems in Engineering
publication date: 2014-10-13T00:00:00Z
review text: Summary: Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90C29
Mathematics Subject Classification ID: 91D30
Mathematics Subject Classification ID: 05C82
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6354149
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 6354149