A Virus Propagation Model for Scale-Free Networks Incorporating Dynamic Behavioral Responses.

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Bibliographic Details
Title:	A Virus Propagation Model for Scale-Free Networks Incorporating Dynamic Behavioral Responses.
Authors:	Zhang, Cong¹ zhangcc334@163.com, Ren, Jianguo² jsnucs1119@163.com, Xu, Yonghong³ xyh8810@126.com
Source:	IAENG International Journal of Applied Mathematics. May2026, Vol. 56 Issue 5, p1832-1845. 14p.
Subjects:	Scale-free network (Statistical physics), Awareness, Computer network security, Epidemiological models, Epidemics
Abstract:	Owing to the widespread use of social platforms and mobile devices, social networks have evolved into complex structures exhibiting scale-free characteristics. In such networks, some highly connected nodes exert significant amplification effects on information diffusion, enabling viruses and malicious links to spread rapidly and posing serious security threats. To overcome the limitations of traditional epidemic models that fail to capture user behavioral heterogeneity and propagation dynamics, this paper proposes an improved propagation model, the SVIWR model, based on the classical SIR framework, tailored for scale-free social networks. The model introduces an alertness mechanism and a weak infection mechanism to describe user awareness enhancement and the decline in activity of infected nodes, respectively. Furthermore, a dynamic parameter adjustment method based on node proportions allows infection and recovery rates to adapt to the state of the system. Together with the alertness and weak infection mechanisms, the model captures dynamic responses in both propagation mechanisms and parameter adaptation, better reflecting real-world spreading behaviors. Using dynamical analysis, the epidemic threshold and stability conditions are derived, and a Lyapunov function is constructed to prove both local and global stability of the equilibrium points. Numerical simulations show that, compared to the traditional SIR model, when alertness and weakening rates are set to 0.2, the steady-state proportion of infected nodes decreases by 48.49%. The proposed mechanism effectively suppresses virus spread in scale-free networks, reduces the infection peak, and provides theoretical support for developing network virus defense strategies. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Owing to the widespread use of social platforms and mobile devices, social networks have evolved into complex structures exhibiting scale-free characteristics. In such networks, some highly connected nodes exert significant amplification effects on information diffusion, enabling viruses and malicious links to spread rapidly and posing serious security threats. To overcome the limitations of traditional epidemic models that fail to capture user behavioral heterogeneity and propagation dynamics, this paper proposes an improved propagation model, the SVIWR model, based on the classical SIR framework, tailored for scale-free social networks. The model introduces an alertness mechanism and a weak infection mechanism to describe user awareness enhancement and the decline in activity of infected nodes, respectively. Furthermore, a dynamic parameter adjustment method based on node proportions allows infection and recovery rates to adapt to the state of the system. Together with the alertness and weak infection mechanisms, the model captures dynamic responses in both propagation mechanisms and parameter adaptation, better reflecting real-world spreading behaviors. Using dynamical analysis, the epidemic threshold and stability conditions are derived, and a Lyapunov function is constructed to prove both local and global stability of the equilibrium points. Numerical simulations show that, compared to the traditional SIR model, when alertness and weakening rates are set to 0.2, the steady-state proportion of infected nodes decreases by 48.49%. The proposed mechanism effectively suppresses virus spread in scale-free networks, reduces the infection peak, and provides theoretical support for developing network virus defense strategies. [ABSTRACT FROM AUTHOR]
ISSN:	19929978