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A Discrete Black Hole Optimization Algorithm for Efficient Community Detection in Social Networks

A Discrete Black Hole Optimization Algorithm for Efficient Community Detection in Social Networks
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Author(s): Mohamed Guendouz (Dr. Moulay Tahar University of Saida, Algeria)
Copyright: 2018
Pages: 21
Source title: Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management
Source Author(s)/Editor(s): Reda Mohamed Hamou (Dr. Tahar Moulay University of Saida, Algeria)
DOI: 10.4018/978-1-5225-3004-6.ch009

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Abstract

In recent years, social networks analysis has attracted the attention of many researchers. Community detection is one of the highly studied problems in this field. It is considered an NP-hard problem, and several algorithms have been proposed to solve this problem. In this chapter, the authors present a new algorithm for community detection in social networks based on the Black Hole optimization algorithm. The authors use the modularity density evaluation measure as a function to maximize. They also propose the enhancement of the algorithm by using two new strategies: initialization and evolution. The proposed algorithm has been tested on famous synthetic and real-world networks; experimental results compared with three known algorithms show the effectiveness of using this algorithm for community detection in social networks.

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