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Information spreading on metapopulation networks with heterogeneous contacting

Author

Listed:
  • Yanyi Nie

    (College of Computer Science, Sichuan University, Chengdu 610065, P. R. China)

  • Liming Pan

    (School of Computer Science and Technology, Nanjing Normal University, Jiangsu 210023, P. R. China)

  • Tao Lin

    (College of Computer Science, Sichuan University, Chengdu 610065, P. R. China)

  • Wei Wang

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, P. R. China)

Abstract

Extensive real-data reveals that individuals exhibit heterogeneous contacting frequency in social systems. We propose a mathematical model to investigate the effects of heterogeneous contacting for information spreading in metapopulation networks. In the proposed model, we assume the number of contacting (NOC) distribution follows a specific distribution, including the normal, exponential, and power-law distributions. We utilize the Markov chain method to study the information spreading dynamics and find that mean and variance display no significant effect on the outbreak threshold for all the considered distributions. Under the same values of NOC distribution’s mean and variance, the information prevalence is largest when the distribution of NOC follows the normal distribution and second-largest for the exponential distribution, the smallest for the power-law distribution. When the distribution of NOC obeys the normal distribution, experimental results show that the information prevalence will decrease with individual contact ability heterogeneity. We observe similar phenomena when the distribution of NOC follows a power-law and exponential distribution. Furthermore, a larger mean of individual contact capacity distribution will result in higher information prevalence.

Suggested Citation

  • Yanyi Nie & Liming Pan & Tao Lin & Wei Wang, 2022. "Information spreading on metapopulation networks with heterogeneous contacting," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 33(03), pages 1-13, March.
    • Handle: RePEc:wsi:ijmpcx:v:33:y:2022:i:03:n:s0129183122500310
    DOI: 10.1142/S0129183122500310
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    Cited by:

    1. Tian, Yang & Tian, Hui & Cui, Qimei & Zhu, Xuzhen, 2024. "Phase transition phenomena in social propagation with dynamic fashion tendency and individual contact," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    2. Meng, Xueyu & Lin, Jianhong & Fan, Yufei & Gao, Fujuan & Fenoaltea, Enrico Maria & Cai, Zhiqiang & Si, Shubin, 2023. "Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).

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