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Abstract
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The identification of communities within social networks holds paramount importance in scientific investigations and social network analyses. Communities refer to compact clusters of nodes that exhibit stronger interconnections with each other rather than with nodes outside their community. By accurately detecting and understanding these communities, we can achieve profound insights into the structure and functionality of social networks. In this research, we introduce an innovative and improved meta-heuristic algorithm specifically designed for detecting community in social networks. Leveraging the powerful gray wolf algorithm as its foundation, our proposed algorithm employs a combination of mutation, combination, and local search operators to significantly enhance its performance. Moreover, to further refine the outcomes, we have integrated the label propagation algorithm into our approach. To measure the efficacy of our proposed algorithm, extensive evaluations were conducted on various social network datasets. The findings consistently validate our algorithm's ability to converge towards optimal results, thanks to its exceptional accuracy and precision. By introducing this advanced meta-heuristic algorithm, we make contributions to the field of social network analysis by providing a robust and efficient solution for community detection. Our algorithm empowers researchers and analysts to acquire a more profound comprehension of social network structures and functions, thereby facilitating improved decision-making and problem-solving.
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