A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion

Hosseinian, Amir Hossein; Teimourpour, Babak; Jamali Hondori, Bagher

doi:10.22054/ims.2019.10376

Document Type : Research Paper

Authors

¹ Ph.D., Industrial Engineering, Faculty of Engineering, Islamic Azad University, Tehran North Branch, Tehran.

² Faculty Member, Department of Industrial and Systems Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran (Corresponding Author: b.teimourpour@modares.ac.ir )

³ Ph.D. Candidate, Industrial Engineering, Faculty of Engineering, Islamic Azad University, Tehran North Branch, Tehran.

https://doi.org/10.22054/ims.2019.10376

Abstract

Detecting existing communities in social networks is a significant process in analyzing these networks. In recent years, the community detection problem has become popular for detecting structures of social networks. Due to high importance of this problem, various algorithms have been developed in the literature to find communities of complex networks. In this research, a hybrid meta-heuristic consisting of the genetic algorithm (GA) and the invasive weed optimization (IWO) method have been proposed which aims to find appropriate and high quality solutions for the community detection problem. In this hybrid method, the initial solutions are generated via the IWO algorithm, and thereafter the optimization process is continued by means of the genetic algorithm. The proposed algorithm is known as the GAIWO. Fitness of solutions is determined in terms of the modularity density criterion. Modularity density has a maximization essence and determines the quality of detected communities. To evaluate the efficiency of the GAIWO, four other methods have been employed and their results have been compared. Comparisons have been made on several networks with different sizes. Input parameters of all algorithms have been tuned by a design of experiments approach. The outputs indicate appropriate efficiency of the proposed algorithm. Validation of the results have been investigated by means of the Normalized Mutual Information (NMI) metric.

Keywords

References

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Business Intelligence Management Studies

A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion

References

References

Volume 8, Issue 29 - Serial Number 29
November 2019
Pages 61-86

A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion

References

References

Volume 8, Issue 29 - Serial Number 29November 2019Pages 61-86

Volume 8, Issue 29 - Serial Number 29
November 2019
Pages 61-86