Opposition-Based Genetic Algorithm for Community Detection in Social Networks
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RESEARCH ARTICLE
Opposition-Based Genetic Algorithm for Community Detection in Social Networks Harish Kumar Shakya1 Bhaskar Biswas2
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Kuldeep Singh2 • Yashvardhan Singh More2
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Received: 12 September 2017 / Revised: 30 August 2020 / Accepted: 12 September 2020 Ó The National Academy of Sciences, India 2020
Abstract This paper proposes an improvised algorithm named modified crossover opposition-based genetic algorithm (MCOBGA) for community detection with the help of genetic algorithm (GA) to discover community structure in social networks. The paper deploys modified crossover and opposition-based initialization along with GA to improve the quality of the community structures. Initialization of the population through opposition-based learning ensures the improved selection of initial population, whereas modified crossover transmits information for improved community structure. The evaluations of proposed algorithm have been done on real-world networks. The experimental results show that MCOBGA has very competitive performance compared with GA with vertex similarity applied to community detection which has been the most similar approach to the proposed algorithm. Experimental results not only demonstrate improvement on convergence rate of the algorithm, but also communities discovered by proposed algorithm (MCOBGA) is highly inclined towards quality, compared to its counterpart. In this paper, we have focused on the community detection problem in the domain of the social network. Community detection is a very basic and hot research problem in complex networks. We have employed the genetic algorithm with modified crossover, opposition-based learning, and matrix encoding technique. We can use this technique
& Harish Kumar Shakya [email protected] 1
Amity University Gwalior, Gwalior 474002, Madhya Pradesh, India
2
Indian Institute of Technology Banaras Hindu University, Uttar Pradesh, Varanasi, India
in agriculture, the health sector, and market data analysis also. Keywords Community detection Genetic algorithm Social network Matrix encoding and opposition-based learning
1 Introduction In recent times, use and study of social networks have been a topic of interest not only to general public but also to groups of researchers and scientist for various reasons [1, 2]. Social networks provide an important source of information news and views about almost all the topics in the world. Social networks are essentially a graph where persons, items or things are represented as nodes which are connected to each other through the edges which show their interactions. Detecting communities in social networks is one of the major tasks in analysis of social networks. The other tasks of social network analysis like viral marketing, influence maximization, etc., can be viewed as an extended application of community detection task. In communities, the nodes are connected to each other through various criteria like mutual interest, likings, disliking, etc. Using the structure of communities, various items or products
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