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An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm

An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm

Khaled Ahmed, Aboul Ella Hassanien, Ehab Ezzat
Copyright: © 2017 |Pages: 14
ISBN13: 9781522522294|ISBN10: 1522522298|EISBN13: 9781522522300
DOI: 10.4018/978-1-5225-2229-4.ch047
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MLA

Ahmed, Khaled, et al. "An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm." Handbook of Research on Machine Learning Innovations and Trends, edited by Aboul Ella Hassanien and Tarek Gaber, IGI Global, 2017, pp. 1062-1075. https://doi.org/10.4018/978-1-5225-2229-4.ch047

APA

Ahmed, K., Hassanien, A. E., & Ezzat, E. (2017). An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm. In A. Hassanien & T. Gaber (Eds.), Handbook of Research on Machine Learning Innovations and Trends (pp. 1062-1075). IGI Global. https://doi.org/10.4018/978-1-5225-2229-4.ch047

Chicago

Ahmed, Khaled, Aboul Ella Hassanien, and Ehab Ezzat. "An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm." In Handbook of Research on Machine Learning Innovations and Trends, edited by Aboul Ella Hassanien and Tarek Gaber, 1062-1075. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2229-4.ch047

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Abstract

Complex social networks analysis is an important research trend, which basically based on community detection. Community detection is the process of dividing the complex social network into a dynamic number of clusters based on their edges connectivity. This paper presents an efficient Elephant Swarm Optimization Algorithm for community detection problem (EESO) as an optimization approach. EESO can define dynamically the number of communities within complex social network. Experimental results are proved that EESO can handle the community detection problem and define the structure of complex networks with high accuracy and quality measures of NMI and modularity over four popular benchmarks such as Zachary Karate Club, Bottlenose Dolphin, American college football and Facebook. EESO presents high promised results against eight community detection algorithms such as discrete krill herd algorithm, discrete Bat algorithm, artificial fish swarm algorithm, fast greedy, label propagation, walktrap, Multilevel and InfoMap.

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