Shetty, R.D.Manoj, T.Bhattacharjee, S.Vasudeva, n.2026-02-032025IEEE Access, 2025, 13, , pp. 183821-183835https://doi.org/10.1109/ACCESS.2025.3624006https://idr.nitk.ac.in/handle/123456789/20606Optimal nodes identification in weighted complex networks is an essential task across diverse areas such as epidemiology, social media, infrastructure, and information diffusion. Traditional centrality measures often fail to capture the nuanced influence of a node when edge weights vary significantly across the network. In the scope of this study, propose a novel centrality measure, Normalized Degree and Weight Centrality (NDWC), that incorporates global scaling and a reducing factor to better assess the importance of nodes in weighted networks. NDWC integrates both structural (degree-based) and strength-based (edge weight) contributions, normalized using global standard deviations to ensure fair comparisons. Furthermore, a reducing factor is introduced to penalize nodes with skewed edge weight distributions, enhancing robustness against local heterogeneity. By combining these elements, NDWC provides a more balanced and representative ranking of nodes. Experimental validation on widely used datasets demonstrates that NDWC outperforms several state-of-the-art methods in identifying influential nodes, particularly in weighted networks. © 2013 IEEE.Arts computingSignal processingCentrality measuresEdge weightsGlobal scalingInfluential nodesNode identificationsNormalized degree and weight centralitySocial informationSocial mediaWeighted complex networksWeighted networksComplex networks