Integrating local and global information to identify influential nodes in complex networks - Mohd Fariduddin Mukhtar; Zuraida Abal Abas; Azhari Samsu Baharuddin; Mohd Natashah Norizan; Wan Farah Wani Wan Fakhruddin; Wakisaka Minato; Amir Hamzah Abdul Rasib; Zaheera Zainal Abidin; Ahmad Fadzli Nizam Abdul Rahman; Siti Haryanti Hairol Anuar (Scientific Reports, 2023)
Abstract Centrality analysis is a crucial tool for understanding the role of nodes in a network, but it is unclear how different centrality measures provide much unique information. To improve the identification of influential nodes in a network, we propose a new method called Hybrid-GSM (H-GSM) that combines the K-shell decomposition approach and Degree Centrality. H-GSM characterizes the impact of nodes more precisely than the Global Structure Model (GSM), which cannot distinguish the importance of each node. We evaluate the performance of H-GSM using the SIR model to simulate the propagation process of six real-world networks. Our method outperforms other approaches regarding computational complexity, node discrimination, and accuracy. Our findings demonstrate the proposed H-GSM as an effective method for identifying influential nodes in complex networks.