Integrating local and global information to identify infuential nodes in complex networks - Abal Abas, Zuraida; Baharuddin, Azhari Samsu; Mukhtar, Mohd Fariduddin; Norizan, Mohd Natashah; Wan Fakhruddin, Wan Farah Wani; Minato, Wakisaka; Abdul Rasib, Amir Hamzah; Zainal Abidin, Zaheera; 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 diferent centrality measures provide much unique information. To improve the identifcation of infuential 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 fndings demonstrate the proposed H-GSM as an efective method for identifying infuential nodes in complex networks.