Identifying influential links to control spreading of epidemics

Binchao Huang, Jin-Xuan Yang and Xin Li

Physica A: Statistical Mechanics and its Applications, 2021, vol. 583, issue C

Abstract: In recent years, epidemics have been raging around the world, causing serious harm to human health, and controlling the spread of the epidemics has become a hot topic. The study of network partition is of great practical significance to control the spread of the epidemics. Network partition is an NP hard problem. Much work has focused on identifying important nodes in complex networks to achieve network partition. Similarly, understanding the importance of links in spreading dynamics in a network can provide ways to hinder or slow down ongoing dynamical phenomena like the spreading of epidemic or the diffusion of information. This paper studies the algorithms to identify influential links in complex networks. Deleting some influential links can divide the network into some components so as to prevent effectively the spreading processes in epidemics. Our algorithms can successfully identify influential links at low computational cost by utilizing the eigenvector centrality measures. The simulated results in some real-world social networks and synthetic networks show that our algorithms are effective. The number of infected individuals can be remarkable reduced by removing influential links.

Keywords: Influential links; Algorithm; Epidemic spreading; Eigenvector centrality (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437121005641
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:583:y:2021:i:c:s0378437121005641

DOI: 10.1016/j.physa.2021.126291

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().