- Assistant Professor, Department of Computer Science, SIES College Of Arts, Science & Commerce (Autonomous), Maharashtra, India
Centrality, a fundamental concept in social network analysis (SNA), plays a pivotal role in understanding the structural dynamics and information flow within networks. This paper offers an extensive examination of centrality metrics and their utilization in diverse fields. We begin by defining centrality and exploring its significance in characterizing the importance of nodes within a network. Subsequently, we delve into the literature Review by different authors and most employed centrality metrics, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, elucidating their underlying mathematical foundations and practical implications. Furthermore, the paper discusses the strengths and limitations of each centrality measure, offering insights into their suitability for different network structures and research objectives. Additionally, we examine real-world applications of centrality in fields such as social sciences, epidemiology, organizational behavior, and information retrieval, highlighting the diverse contexts in which centrality analysis proves invaluable. This review serves as a comprehensive resource for researchers, practitioners, and enthusiasts in the field of social network analysis, offering a nuanced understanding of centrality measures and their broad-ranging applications across disciplines. Major focus is to make students in undergraduate degree and post-graduation about centrality measures in social network analysis.
Keywords: Degree centrality, betweenness centrality, SNA, centrality metrics
[This article belongs to International Journal of Data Structure Studies(ijdss)]
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|Received||September 20, 2023|
|Accepted||October 3, 2023|
|Published||October 25, 2023|