Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks

Qian Zhang,Xianyong Li,Yajun Du,Jian Zhu,Miguel Ángel López

doi:10.1155/2021/5615096

Qian Zhang, Xianyong Li + Show 3 more

Open Access

https://doi.org/10.1155/2021/5615096

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Abstract

Due to the inequality of users’ (nodes’) status and the influence of external forces in the progress of the information propagation in a social network, the infected nodes hold different levels of propagation capacity. For this reason, the infected nodes are classified into two categories: the high influential infected nodes and the ordinary influential infected nodes which separately account for 20% and 80% by Pareto’s principle. By borrowing the SEIR epidemic model, this paper proposes an SE2IR information propagation model. Meanwhile, the global asymptotical stabilities of the spread-free equilibrium point and local spread equilibrium point are proved for this model. This paper also puts forward a series of information control strategies including perceived values of users, social reinforcement intensity, and information timeliness in the social network. Through simulation experiments without or with control strategies on a real company e-mail network dataset, this paper verifies the stability and correctness of the model and the feasibility and effectiveness of the control strategies in the information propagation process, presenting that the model is closer to the real process of the information propagation in the social network.

Highlights

With the rapid development of the Internet, more and more people are active online; sharing their hobbies, lives, and feelings; forwarding some news; spreading or reviewing some ideas; advertising their products; and so forth. eir propagation behaviors would give rise to a new topic and even public opinion in online social networks
Wang et al [12] proposed the 2SI2R rumor propagation model which discussed two types of rumors spread among the crowd at the same time. ey employed the same approach of the SIR epidemic model to analyze the dynamic properties
In order to really reflect the trends, based on the proposed influencing factors, the state transition probabilities of the proposed SE2IR model are amply interpreted as follows: α1, α2: in a social network, the process that a susceptible node propagates information to its neighbors is mainly leveraged by the perceived value of the node for the information and the information timeliness; when the information is good for a node, the node actively spreads the information to its neighbors; the newer the information is, the more attractive it is to the user

Summary

Introduction

With the rapid development of the Internet, more and more people are active online; sharing their hobbies, lives, and feelings; forwarding some news; spreading or reviewing some ideas; advertising their products; and so forth. eir propagation behaviors would give rise to a new topic and even public opinion in online social networks. Considering the exposed nodes may become immune nodes at a certain probability, Liu et al [21] presented an SEIR rumor spread model in the heterogeneous network In view of their model, they discussed the global dynamic behavior of no-rumors balanced set and two immunization strategies of rumors diffusion. Ey proposed the real-time optimization strategy and the pulse spreading truth and continuous blocking rumor strategy to restrain the rumor spreading Another modeling way focuses on the interaction between the user and the effect of network topological structure on the information propagation process, including Ising model and Deffuant model [27, 28]. As far as we know, a few papers have adopted Pareto’s principle, users’ status, and user influence of external forces to the information propagation and control in the social network.

The Analysis of the SE2IR Information Propagation Model

Numerical Simulation

Findings

Conclusion