*Corresponding Author: harish.shakya@jaipur.manipal.edu 342 DOI: https://doi.org/10.52756/ijerr.2024.v46.027 An Elitism-based Novel Approach for Community Detection in Social Networks Ranjana Sikarwar 1 *, Shyam Sunder Gupta 1 and Harish Kumar Shakya 2 1 Department of Computer Science and Engineering, Amity University, Gwalior, Madhya Pradesh, India; 2 Department of Artificial Intelligence & Machine Learning, Manipal University, Jaipur, Rajasthan, India E-mail/Orcid Id: RS, ranjana.sik@gmail.com, https://orcid.org/0000-0002-9640-1145; SSG, ssgupta@gwa.amity.edu, https://orcid.org/0000-0001-6841-3897; HKS, harish.shakya@jaipur.manipal.edu, https://orcid.org/0000-0002-5401-3507 Introduction Social network analysis (SNA) is used to study social structures by analyzing the relationships between individuals and communities. The domain of SNA combines theory, methods, and computer technology to explore social networks and their interaction patterns using network and graph theory. The study observed it in the form of networked structures with nodes as users, objects, or things acting in a network and edges as relationships connecting these nodes (Fortunato, 2010; Pizzuti, 2018). SNA offers tools for analyzing social networks, identifying key factors and relationships, and studying the dynamics of social processes. It is used in various fields, such as sociology, anthropology, psychology, organizational studies, and computer science, for observing social influence, cooperation, diffusion of innovations, social support, and disease transmission. Social networks play a pivotal role in driving social change by enabling the rapid spread of ideas, collective action, and mobilization. They offer platforms for individuals to share information, voice concerns, and organize movements, fostering awareness and activism. For example, social networks have been instrumental in movements like the Arab Spring, Black Lives Matter, and environmental protests, as they allow people to connect across borders, coordinate activities, and amplify voices that may otherwise be marginalized. The ability to rapidly disseminate information and rally support through online platforms has reshaped how social change occurs, making it more global and participatory (Camacho et al., 2020). Article History: Received: 24 th Aug., 2024 Accepted: 25 th Dec., 2024 Published: 30 th Dec., 2024 Abstract: The detection of communities is an important problem in social network analysis, which has applications in various domains like sociology, biology, computer science, and marketing. In this context, genetic algorithms have proven to be effective in detecting communities by optimizing the modularity score of the network. The proposed work in this research paper uses an elitism-based genetic algorithm with some modified crossover and mutation techniques to detect communities in social networks. The proposed methodology incorporates the concepts of elitism, N-point crossover, and inverse mutation to enhance the effectiveness of genetic algorithms in solving optimization problems. The idea introduced in this article significantly extends the current understanding of optimization and evolutionary algorithms. We present an advanced methodology that leverages various genetic operators to improve the performance of a genetic algorithm in solving community detection problems in complex networks. Numerous research papers have extensively showcased the practicality of evolutionary and swarm-based algorithms in addressing real-world problems across diverse domains like viral marketing, link prediction, influence maximization, political polarization, etc. Hybridizing these algorithms with other optimization techniques has improved the performance and convergence speed, leading to enhanced optimization outcomes. Keywords: Genetic algorithm, Elitism, Modularity, Community detection, Social networks, Multi-objective, Swarm- intelligent techniques, Convergence, Local optima How to cite this Article: Ranjana Sikarwar, Shyam Sunder Gupta and Harish Kumar Shakya (2024). An Elitism-based Novel Approach for Community Detection in Social Networks. International Journal of Experimental Research and Review, 46, 342-354. DOI: https://doi.org/10.52756/ijerr.2024.v46.027 Int. J. Exp. Res. Rev., Vol. 46: 342-354 (2024)