A Virus Propagation Model for Scale-Free Networks Incorporating Dynamic Behavioral Responses.
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| Title: | A Virus Propagation Model for Scale-Free Networks Incorporating Dynamic Behavioral Responses. |
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| 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] |
| Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193517541 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Virus Propagation Model for Scale-Free Networks Incorporating Dynamic Behavioral Responses. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Cong%22">Zhang, Cong</searchLink><relatesTo>1</relatesTo><i> zhangcc334@163.com</i><br /><searchLink fieldCode="AR" term="%22Ren%2C+Jianguo%22">Ren, Jianguo</searchLink><relatesTo>2</relatesTo><i> jsnucs1119@163.com</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Yonghong%22">Xu, Yonghong</searchLink><relatesTo>3</relatesTo><i> xyh8810@126.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. May2026, Vol. 56 Issue 5, p1832-1845. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Scale-free+network+%28Statistical+physics%29%22">Scale-free network (Statistical physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Awareness%22">Awareness</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+security%22">Computer network security</searchLink><br /><searchLink fieldCode="DE" term="%22Epidemiological+models%22">Epidemiological models</searchLink><br /><searchLink fieldCode="DE" term="%22Epidemics%22">Epidemics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1832 Subjects: – SubjectFull: Scale-free network (Statistical physics) Type: general – SubjectFull: Awareness Type: general – SubjectFull: Computer network security Type: general – SubjectFull: Epidemiological models Type: general – SubjectFull: Epidemics Type: general Titles: – TitleFull: A Virus Propagation Model for Scale-Free Networks Incorporating Dynamic Behavioral Responses. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Cong – PersonEntity: Name: NameFull: Ren, Jianguo – PersonEntity: Name: NameFull: Xu, Yonghong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19929978 Numbering: – Type: volume Value: 56 – Type: issue Value: 5 Titles: – TitleFull: IAENG International Journal of Applied Mathematics Type: main |
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