Rank the spreading influence of nodes using dynamic Markov process
OPEN ACCESS
Author / Producer
Date
2023-02
Publication Type
Journal Article
ETH Bibliography
yes
OPEN ACCESS
Data
Rights / License
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
25 (2)
Pages / Article No.
23014
Publisher
IOP Publishing
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Influence of nodes; Complex networks; Markov process
Organisational unit
02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.