Regular Research ArticlesNo Access

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Key Laboratory of Media Convergence, Technology and Communication, Lanzhou, Gansu 730030, P. R. China

JIE TANG

https://orcid.org/0009-0006-2907-6924

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Key Laboratory of Media Convergence, Technology and Communication, Lanzhou, Gansu 730030, P. R. China

E-mail Address: tang121413@126.com

Corresponding author.

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HUAYAN PEI

https://orcid.org/0000-0002-0145-4042

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Key Laboratory of Media Convergence, Technology and Communication, Lanzhou, Gansu 730030, P. R. China

E-mail Address: pei123com@126.com

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, and

WENWEN CHANG

https://orcid.org/0000-0002-4984-2554

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Key Laboratory of Media Convergence, Technology and Communication, Lanzhou, Gansu 730030, P. R. China

E-mail Address: changww2013@126.com

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https://doi.org/10.1142/S0219525925500055Cited by:0 (Source: Crossref)

Abstract

Considering that rumors propagation is affected by many factors in real life, based on the SIRS infectious disease model in complex networks, an extended ISRI rumor propagation model is proposed by using the probability function to define the influence mechanisms such as trust mechanism, and suspicion mechanism. First, dynamic equations are established for homogeneous and heterogeneous networks, and the rumor and rumor-free equilibrium points in the two networks are analyzed, respectively. Then, the basic reproduction number $R_{0}$ $R_{0}$ is obtained by using the next generation matrix and derivative calculation methods. Next, the lyapunov function is constructed to discuss the local stability and global stability of the equilibrium point, and the influence of different parameters on the basic reproduction number $R_{0}$ $R_{0}$ . In addition, we selected ER network and BA network and found that population flow has a significant impact on the speed and scale of rumor propagation. At the same time, the trust mechanism can improve the propagation speed and scale, while the skepticism mechanism can inhibit the propagation speed, and it is more obvious in the BA network. The interaction between these mechanisms further affects the propagation characteristics of rumors in the network.

Keywords:

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