Understanding the structural properties of protein-protein interaction (PPI) networks is essential for identifying key proteins that play a central role in the stability and function of biological systems. In this study, we evaluate four proposed centrality measures—Max-Degree Weighted Betweenness Centrality, Max Eigenvector-Weighted Betweenness Centrality, Degree-Weighted Betweenness Centrality, and Eigenvector-Weighted Betweenness Centrality —in the context of the dengue virus PPI network. The new measures incorporate degree and eigenvalue weights into traditional betweenness centrality calculations, enhancing the ability to capture both local and global network structures. By integrating node connectivity (degree) and network-wide influence (eigenvector centrality) into betweenness calculations, these measures aim to provide a more nuanced understanding of node importance in complex networks. A comparative analysis is conducted with 24 established centrality measures, including degree, closeness, and betweenness centrality, among others. Pearson correlation analysis reveals strong interdependence among the proposed metrics, indicating their robustness in identifying influential nodes. The study highlights the subtle but meaningful differences between these metrics, providing complementary insights into network topology. The findings demonstrate that these centrality measures effectively rank key nodes, offering potential targets for therapeutic interventions aimed at disrupting critical interactions within the dengue virus network. This work explores to network-based methods for disease management by a deep understanding of centrality measures in viral PPIs.