A multi-objective approach for community detection in complex network

Author

Shi, Chuan ; Zhong, Cha ; Yan, Zhenyu ; Cai, Yanan ; Wu, Bin

Author_Institution

Beijing Key Lab. of Intell. Telecommun. Software & Multimedia, Beijing Univ. of Posts & Telecommun., Beijing, China

fYear

2010

fDate

18-23 July 2010

Firstpage

1

Lastpage

8

Abstract

Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). Correspondingly, a special Multi-Objective Evolutionary Algorithm (MOEA) is designed to solve the MOP and two model selection methods are proposed. The experiments in artificial and real networks show that the multi-objective community detection algorithm is able to discover more accurate community structures.

Keywords

complex networks; evolutionary computation; network theory (graphs); complex network; multiobjective community structure detection algorithm; multiobjective evolutionary algorithm; multiobjective optimization problem; optimization criteria; two model selection method; Clustering algorithms; Communities; Complex networks; Detection algorithms; Evolutionary computation; Optimization; Partitioning algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2010 IEEE Congress on

Conference_Location

Barcelona

Print_ISBN

978-1-4244-6909-3

Type

conf

DOI

10.1109/CEC.2010.5585987

Filename

5585987