Evolutionary algorithm and modularity for detecting communities in networks

Saoud Bilal and Moussaoui Abdelouahab

Physica A: Statistical Mechanics and its Applications, 2017, vol. 473, issue C, 89-96

Abstract: Evolutionary algorithms are very used today to resolve problems in many fields. There are few community detection methods in networks based on evolutionary algorithms. In our paper, we develop a new approach of community detection in networks based on evolutionary algorithm. In this approach we use an evolutionary algorithm to find the first community structure that maximizes the modularity. After that we improve the community structure through merging communities to find the final community structure that has the high value of modularity. We provide a general framework for implementing our approach. Compared with the state of art algorithms, simulation results on computer-generated and real world networks reflect the effectiveness of our approach.

Keywords: Community detection; Networks; Evolutionary algorithms; Normalized mutual information; Modularity (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437117300249
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:473:y:2017:i:c:p:89-96

DOI: 10.1016/j.physa.2017.01.018

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().