Volume 142, Number 6, June 2023
|Number of page(s)||7|
|Section||Statistical physics and networks|
|Published online||15 June 2023|
A mixed strength decomposition method for identifying critical nodes by decomposing weighted social networks
1 School of Economics and Management, Harbin Engineering University - Harbin, China
2 Centre for Big Data and Business Intelligence, Harbin Engineering University - Harbin, China
(a) E-mail: firstname.lastname@example.org (corresponding author)
Received: 9 December 2022
Accepted: 30 May 2023
Identifying critical nodes is an efficient strategy for preventing the dynamics of risk dissemination. The properties of edges connecting to the removed nodes are assumed to be the same by many decomposition methods. However, the edge weights are always different in weighted social networks since they have certain practical implications. In this study, a mixed strength decomposition (MSD) method is proposed to identify critical nodes in weighted social networks. This method aims to address the issue of not accounting for the information on removed nodes by considering both residual strength and exhausted strength. Three experimental analyses —the monotonicity test, Susceptible-Infected (SI) diffusion simulation, and successive node removal experiments— conducted on six real-world networks demonstrate that the MSD method has a competitive performance in identifying critical nodes, which overcomes the instability of the node strength and the degeneracy of the s-core method.
© 2023 EPLA
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.