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Rank the spreading influence of nodes using dynamic Markov process


Lin, Jianhong; Chen, Bo-Lun; Yang, Zhao; Liu, Jian-Guo; Tessone, Claudio J (2023). Rank the spreading influence of nodes using dynamic Markov process. New Journal of Physics, 25(2):023014.

Abstract

Ranking the spreading influence of nodes is of great importance in practice and research. The key to ranking a node’s spreading ability is to evaluate the fraction of susceptible nodes being infected by the target node during the outbreak, i.e. the outbreak size. In this paper, we present a dynamic Markov process (DMP) method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of the Markov process, this method solves the problem of nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node being infected by its infected neighbors. We have employed the susceptible-infected-recovered and susceptible-infected-susceptible models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes’ outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process.

Abstract

Ranking the spreading influence of nodes is of great importance in practice and research. The key to ranking a node’s spreading ability is to evaluate the fraction of susceptible nodes being infected by the target node during the outbreak, i.e. the outbreak size. In this paper, we present a dynamic Markov process (DMP) method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of the Markov process, this method solves the problem of nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node being infected by its infected neighbors. We have employed the susceptible-infected-recovered and susceptible-infected-susceptible models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes’ outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
08 Research Priority Programs > Social Networks
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > General Physics and Astronomy
Scope:Discipline-based scholarship (basic research)
Language:English
Date:10 February 2023
Deposited On:15 Feb 2024 10:58
Last Modified:31 May 2024 01:55
Publisher:IOP Publishing
Number of Pages:23014
ISSN:1367-2630
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1088/1367-2630/acb590
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)