Abstract
The study of the laws and influencing factors of information dissemination in social networks is critical to analyzing the spread of public opinion, preventing the spread of rumors, and guiding information transmission. This paper addresses the shortcomings of the traditional SEIR model. We establish the SETQR model and use the probability theorem to derive the law of information propagation. Furthermore, the equilibrium point and the basic regeneration number of the SETQR model are obtained using differential dynamics and the regenerative matrix method. The stability of the SETQR model at the equilibrium point is derived theoretically. Finally, experimental verification is conducted. The simulation results indicate that the SETQR model achieves local stability at the equilibrium point, which is consistent with the results of the theoretical analysis. Through further simulation, the effects of time lag, containment, and forgetting mechanisms on the speed of information dissemination and the time required for the network to reach equilibrium are analyzed.
Highlights
Complex networks can be used to represent complex systems in nature and the real world
When information is transmitted through social networks, only a small portion is spread widely while most information is spread in a small area or is overshadowed by a large amount of information flow without being spread
Exploring the mechanism of information dissemination in social networks, establishing an information dissemination model and analyzing its stability can greatly aid in accurately predicting the trend of network information dissemination, preventing the spread of rumors, and guiding information transmission
Summary
Complex networks can be used to represent complex systems in nature and the real world. The established SETQR information propagation model is based on the time lag, containment, and forgetting mechanisms. (1) In order to ensure that the network model closely resembles the actual situation, the SETQR model considers the following factors: i) Infected nodes can selectively disseminate information; ii) There are nodes in the network that are offline and the processing of information has a time lag; and iii) The immune node may be activated again after a long interval and resume receiving information. The simulation results indicate that the SETQR model has local asymptotic stability in both the R0 < 1 and R0 > 1 cases, which are consistent with the theoretical derivation. Chen: SETQR Propagation Model for Social Networks process and network equilibrium state are simulated, and the simulation results are analyzed.
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