Journal Article

Integrating local and global information to identify influential nodes in complex networks

Abal Abas, Zuraida, Minato, Wakisaka, Fakhruddin, Wan Farah Wani Wan, Mukhtar, Mohd Fariduddin, Baharuddin, Azhari Samsu, Norizan, Mohd Natashah, Rasib, Amir Hamzah Abdul, Anuar, Siti Haryanti Hairol, Rahman, Ahmad Fadzli Nizam Abdul, Abidin, Zaheera Zainal

2023

Overview

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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/705/1042 / 639/705/117 / Accuracy / Algorithms / Humanities and Social Sciences / Influence / multidisciplinary / Nodes / Performance evaluation / Science / Science (multidisciplinary)