Welcome to the InfoSci Platform
Reference Hub10
A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks

A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks

Ramadan Babers, Aboul Ella Hassanien
Copyright: © 2017 |Volume: 8 |Issue: 1 |Pages: 13
ISSN: 1947-959X|EISSN: 1947-9603|EISBN13: 9781522512974|DOI: 10.4018/IJSSMET.2017010104
Cite Article Cite Article

MLA

Babers, Ramadan, and Aboul Ella Hassanien. "A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks." IJSSMET vol.8, no.1 2017: pp.50-62. http://doi.org/10.4018/IJSSMET.2017010104

APA

Babers, R. & Hassanien, A. E. (2017). A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 8(1), 50-62. http://doi.org/10.4018/IJSSMET.2017010104

Chicago

Babers, Ramadan, and Aboul Ella Hassanien. "A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) 8, no.1: 50-62. http://doi.org/10.4018/IJSSMET.2017010104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.