Centrality is an essential concept in network sciences, which evolved from graph theory. The use of centrality measures allows the identification of the dominant elements in a network. The definition of centrality and the first associated interventions were developed for social network analysis and have since been applied to other fields. However, as the number of publications has steadily increased over the years, it is becoming increasingly difficult to maintain the growing volume of scholarly publications. Research and publications are extensively developed and have no proper record. Therefore, this study executes a bibliometric analysis on centrality measures, as well as its popular method: Betweenness Centrality, Closeness Centrality, Eigenvector Centrality, and Degree Centrality, by reviewing a database that makes its research, awareness, global evolution, and potential trend lines available. Data were obtained from the Scopus database arranging from 2014 to 2024. The bibliometric analysis provides a valuable overview of the evolution of centrality measures in terms of the number of publications, most cited publications, most significant collaboration countries, and current trends in centrality. This study of bibliometric analysis on centrality measures have not been carried out yet. Therefore, prediction on the hotspots and current trends within certain research areas and methods would give researchers insight into further its development.
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