Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks

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.

Modeling and quantifying the user influence on information dissemination in the Chinese Sina-Microblog

“…We adopt the basic structure in the SFI model 17 and introduce the user classification based on various user influences into our established model. Table 1 shows all parameters and corresponding interpretations in our user-influence susceptibleforwarding-immune (UI-SFI) model, and the dynamic system takes the form as eq(1):…”

SCWIR: A rumor propagation model considering user waiting behavior

“…proposed a dynamic evolutionary model, PN-UHTR, which combines individual behavioral layer and information dissemination layer coupled in both directions, determined the probability of nodes reaching the steady state and simulated and analyzed the effects of different strategies on information dissemination in the hyper-network. Zhang et al(2021a) constructed the SE2IR information dissemination model by considering information control strategies such as users' perceived value, social reinforcement intensity, and information timeliness in social networks. Markovich et al(2020) considered tight centrality as a measure of node leadership, and then investigated the impact of node and community leadership on information dissemination.…”

An effective ISDPR rumor propagation model on complex networks

“…Based on the SEIR epidemic model, Zhang et al 11 divided infected person into two states‐trusted state and questioned state, and established SETQR (susceptible‐exposed‐trusted‐questioned‐recovered) information propagation model. Zhang et al 12 considered that different nodes have different levels of communication ability, and proposed an SE2IR information propagation model. Li et al 13 combined with many factors, proposed a new rumor propagation model for qualitative and quantitative analysis.…”