Layered SIRS model of information spread in complex networks

Yuexia Zhang and Dawei Pan

Applied Mathematics and Computation, 2021, vol. 411, issue C

Abstract: In complex network research, the infectious disease model is often used to study transmission mechanisms and interference factors of information; consequently, they are essential for the prediction and control of information transmission. The conventional SIRS epidemic model has a wide range of applications and is theoretically mature. However, it does not stratify the nodes in a network, and fails to reflect the characteristics of different nodes. To solve this problem, we propose a layered SIRS information transmission model (L-SIRS) . Depending on the node influence, we assign the nodes in the network to high- and low-influence layers and establish an intra- and inter-layer information transmission mechanism. The transmission threshold and equilibrium point of this model are analyzed theoretically. To reduce the transmission of online public opinions, two kinds of information transmission interference strategies, namely, information blocking and information dredging, are designed to study their influence on information transmission. Finally, combine with practice, the simulation results indicate that the L-SIRS model can more accurately describe network information transmission and both information blocking and information dredging can effectively inhibit the spread of information.

Keywords: Complex networks; Infectious disease model; Information transmission; Interference strategy; Propagation threshold; Global stability (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)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:411:y:2021:i:c:s0096300321006135

DOI: 10.1016/j.amc.2021.126524

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