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Social contagions on multiplex networks with heterogeneous population

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  • Zhu, Shu-Shan
  • Zhu, Xu-Zhen
  • Wang, Jian-Qun
  • Zhang, Zeng-Ping
  • Wang, Wei

Abstract

In this paper, we study the effects of heterogeneous population on the dynamics of social contagions on multiplex networks. We assume a fraction of f nodes with a higher adoption threshold T>1, and the remaining fraction of 1−f nodes with adoption threshold 1. A social contagion model is proposed to describe the social contagions, in which a susceptible node adopting the contagion only when its received accumulated information is larger than the adoption threshold in either subnetwork. With an edge-based compartmental approach and extensive numerical simulations, we find that the system exhibits a continuous phase transition for small values of f, while shows a hybrid phase transition for relatively large values of f and T. For homogeneous multiplex networks the hybrid phase transition occurs, while there is only a continuous phase transition for heterogeneous multiplex networks. Our theoretical predictions agree well with numerical simulations.

Suggested Citation

  • Zhu, Shu-Shan & Zhu, Xu-Zhen & Wang, Jian-Qun & Zhang, Zeng-Ping & Wang, Wei, 2019. "Social contagions on multiplex networks with heterogeneous population," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 105-113.
    • Handle: RePEc:eee:phsmap:v:516:y:2019:i:c:p:105-113
    DOI: 10.1016/j.physa.2018.10.010
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    References listed on IDEAS

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    Cited by:

    1. Li, Yang & Sun, Hao & Xiong, Wanda & Xu, Genjiu, 2021. "Belief model of complex contagions on random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    2. Wei Duan, 2021. "Matrix-Based Formulation of Heterogeneous Individual-Based Models of Infectious Diseases: Using SARS Epidemic as a Case Study," IJERPH, MDPI, vol. 18(11), pages 1-20, May.
    3. Sang, Chun-Yan & Liao, Shi-Gen, 2020. "Modeling and simulation of information dissemination model considering user’s awareness behavior in mobile social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).

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