Analysis of two-layer network SA1A2R1R2 model under the influence of competitive information and asymmetric activity

Huo, Liang’an, Mengyu Pan and Jiafeng Gu

Physica A: Statistical Mechanics and its Applications, 2025, vol. 676, issue C

Abstract: The dissemination of competing vaccine-related information across various social media platforms profoundly influences the process of adopting vaccination behaviors. At the same time, variations in individual activity levels across various information platforms affect the accumulation of information, subsequently shaping the vaccination behavior adoption. This paper presents a two-layer SA1A2R1R2 network model to emulate the adoption of vaccination behavior under the influence of competitive information accumulation and an asymmetric activeness interaction mechanism. To capture the differences in channels of information diffusion, distinct social reinforcement mechanisms are employed in the two-layer network to describe the information accumulation process. A theoretical approach to the model is performed utilizing edge-based compartmental theory. We provide numerical simulations of the model and offers indications of vaccination strategies. The results reveal that increased activity in online communication channels significantly contributes to the final adoption scale of vaccination behaviors compared to offline communication channels. Moreover, there exists a Pareto optimum of two-layer activity to maximize the effectiveness of vaccine promotion. Furthermore, local social reinforcement for competing messages proves more effective in promoting vaccination behavior than global message reinforcement.

Keywords: Vaccination behavior adoption; Competitive information; Asymmetric activity; Social reinforcement; Two-layer network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:676:y:2025:i:c:s0378437125005369

DOI: 10.1016/j.physa.2025.130884

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