Hybrid Global Structure Model for Unraveling Influential Nodes in Complex Networks

In graph analytics, the identification of influential nodes in real-world networks plays a crucial role understanding network dynamics and enabling various applications. However, traditional centrality metrics often fall short capturing interplay between local global information. To address this limitation, Global Structure Model (GSM) its improved version (IGSM) have been proposed. Nonetheless, these models still lack an adequate representation path length. This research aims to enhance existing approaches by developing hybrid model called H-GSM. The H-GSM algorithm integrates GSM framework with measurements, specifically Degree Centrality (DC) K-Shell (KS). By incorporating additional measures, strives improve accuracy identifying complex networks. evaluate effectiveness model, datasets are employed, comparative analyses conducted against techniques. results demonstrate that outperforms techniques, showcasing enhanced performance nodes. As future directions, it is proposed explore different combinations index styles measures within framework.