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Propagation characteristic of adoption thresholds heterogeneity in double-layer networks with edge weight distribution

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  • Tian, Yang
  • Zhu, Xuzhen
  • Yang, Qiwen
  • Tian, Hui
  • Cui, Qimei

Abstract

People normally show different relation intensity and adoption behaviors, which can affect information spreading on social networks. To describe the two phenomena, we consider edge weight distribution and adoption thresholds heterogeneity on the networks separately, where the adoption thresholds of nodes obey truncated gaussian distribution. Therefore, we propose a double-layer network model with edge weight distribution to explore the impact of heterogeneous adoption thresholds of nodes on information spreading. And we also develop an edge-based compartmental theory to analyze the information spreading mechanism. Through numerical simulations and theoretical analysis, we find that increasing the values of mean can suppress information spreading, and the impact of the values of standard deviation on information spreading depends on the values of mean. Specifically, for a small value of mean, the size of information spreading will decrease with the increase of the value of standard deviation. Meanwhile, when the values of standard deviation are fixed, the size of information spreading will increase continuously with the increase of transmission probability. For a large value of mean, the size of information spreading will increase first and then decrease with the increase of the value of standard deviation. In addition, this paper shows that the theoretical method agrees with the numerical simulations in the experimental results.

Suggested Citation

  • Tian, Yang & Zhu, Xuzhen & Yang, Qiwen & Tian, Hui & Cui, Qimei, 2022. "Propagation characteristic of adoption thresholds heterogeneity in double-layer networks with edge weight distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    • Handle: RePEc:eee:phsmap:v:591:y:2022:i:c:s0378437121009560
    DOI: 10.1016/j.physa.2021.126768
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    References listed on IDEAS

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    1. Suo, Qi & Guo, Jin-Li & Shen, Ai-Zhong, 2018. "Information spreading dynamics in hypernetworks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 475-487.
    2. Yu Chen & Wei Wang & Jinping Feng & Ying Lu & Xinqi Gong, 2020. "Maximizing multiple influences and fair seed allocation on multilayer social networks," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-19, March.
    3. Charles Murphy & Edward Laurence & Antoine Allard, 2021. "Deep learning of contagion dynamics on complex networks," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Peng, Hao & Peng, Wangxin & Zhao, Dandan & Wang, Wei, 2020. "Impact of the heterogeneity of adoption thresholds on behavior spreading in complex networks," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    5. Chen, Ling-Jiao & Chen, Xiao-Long & Cai, Meng & Wang, Wei, 2018. "Complex contagions with social reinforcement from different layers and neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 516-525.
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    1. Tian, Yang & Tian, Hui & Cui, Yajuan & Zhu, Xuzhen & Cui, Qimei, 2023. "Influence of behavioral adoption preference based on heterogeneous population on multiple weighted networks," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    2. Huo, Liang’an & Yu, Yue, 2023. "The impact of the self-recognition ability and physical quality on coupled negative information-behavior-epidemic dynamics in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).

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