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