An extended improved global structure model for influential node identification in complex networks

doi:10.1088/1674-1056/ac380d

Abstract Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses. Many classical approaches have been proposed by researchers regarding different aspects. To explore the impact of location information in depth, this paper proposes an improved global structure model to characterize the influence of nodes. The method considers both the node's self-information and the role of the location information of neighboring nodes. First, degree centrality of each node is calculated, and then degree value of each node is used to represent self-influence, and degree values of the neighbor layer nodes are divided by the power of the path length, which is path attenuation used to represent global influence. Finally, an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes. In this paper, the propagation process of a real network is obtained by simulation with the SIR model, and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy. The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.

Keywords: complex network influential nodes extended improved global structure model SIR model

Received: 02 August 2021
Revised: 29 October 2021
Accepted manuscript online: 10 November 2021

PACS:

89.75.Fb

(Structures and organization in complex systems)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 11975307).

Corresponding Authors: Lun-Wen Wang
E-mail: wanglunwenmust@nudt.edu.cn

Cite this article:

Jing-Cheng Zhu(朱敬成) and Lun-Wen Wang(王伦文) An extended improved global structure model for influential node identification in complex networks 2022 Chin. Phys. B 31 068904

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