Advancements in Centrality Measures for Complex Networks: Narrative Review

Abstract

Centrality measures are pivotal in the analysis of complex networks, providing insights into the structural importance of nodes. Traditional metrics such as degree, closeness, betweenness, and eigenvector centrality have been extensively utilized. However, the heterogeneous nature of real-world networks necessitates the development of more nuanced measures . This narrative review explores recent advancements in centrality metrics, focusing on their applicability to networks with diverse topologies and the incorporation of multidimensional factors. Mathematical formulations and comparative evaluations are also discussed, along with emerging applications in various domains. Additionally, the study highlights limitations and future research directions in network science.