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@article{174222,
        author = {Alapati Iswarya and Dendukuri Varshitha and Patta JanardhanaRao and Jampana Anil Kumar and D.Anusha},
        title = {Identifying Influential Nodes in Complex Networks: Combining Global and Local Views},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3590-3594},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174222},
        abstract = {Influential node identification in complex networks is an important problem with implications in disease spread, viral marketing, rumor spreading, and opinion tracking. Classical centrality measures tend to miss the subtle significance of nodes by considering only global or local structural features. This article presents two measures of centrality: Global Relative Average Centrality (GRAC) and Local Relative Average Centrality (LRAC). GRAC measures the relative change in a node's centrality at the global level of the network after its removal, and LRAC measures the effect of node removal on local neighborhood structure. With the SIR model, we illustrate how GRAC and LRAC perform better than basic centrality measures in finding influential nodes and provide a more holistic measure of node importance in complex networks.},
        keywords = {Influential Nodes, Centrality Measures, Global Relative Average Centrality (GRAC), Local Relative Average Centrality (LRAC), Susceptible-Infected-Recovered (SIR) Model.},
        month = {March},
        }