Integrating local and global information to identify influential nodes in complex networks - Mukhtar, Mohd Fariduddin; Abal Abas, Zuraida; Baharuddin, Azhari Samsu; Norizan, Mohd Natashah; Wan Fakhruddin, Wan Farah Wani; Minato, Wakisaka; Abdul Rasib, Amir Hamzah; Abidin, Zaheera Zainal; Abdul Rahman, Ahmad Fadzli Nizam; Hairol Anuar, Siti Haryanti (Nature Research, 2023)
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.