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

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Hosseinian Amir Hossein; Teimourpour Babak; Jamali Hondori Bagher

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Journal Paper

Paper Information

Journal: JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES Year:2019 | Volume:8 | Issue:29 Page(s): 61-86

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Title

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

Author(s)

Hosseinian Amir Hossein | Teimourpour Babak | Jamali Hondori Bagher | Issue Writer Certificate

Keywords

Community detection

Soft Computing

Invasive weed optimization

Modularity density

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.

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APA: Copy

Hosseinian, Amir Hossein, Teimourpour, Babak, & Jamali Hondori, Bagher. (2019). A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion. JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES, 8(29 ), 61-86. SID. https://sid.ir/paper/268113/en

Vancouver: Copy

Hosseinian Amir Hossein, Teimourpour Babak, Jamali Hondori Bagher. A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion. JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES[Internet]. 2019;8(29 ):61-86. Available from: https://sid.ir/paper/268113/en

IEEE: Copy

Amir Hossein Hosseinian, Babak Teimourpour, and Bagher Jamali Hondori, “A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion,” JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES, vol. 8, no. 29 , pp. 61–86, 2019, [Online]. Available: https://sid.ir/paper/268113/en

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