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Understanding the diversity on power-law-like degree distribution in social networks

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  • Xu, Xiao-Ting
  • Wang, Nianxin
  • Bian, Jun
  • Zhou, Bin

Abstract

The diversity of power-law-like degree distribution in social networks has been discovered in a large number of empirical studies. Analyzing the origin of power-law-like the degree distribution diversity is greatly important for understanding the law of human social interaction. In our work, the diversity of power-law-like degree distribution is demonstrated empirically in social networks. The origin of the degree distribution diversity is analyzed from the point of the social stratification and the bidirectional preferential attachment among individuals. We proposed a model to reproduce the diversity of degree distribution in social networks, and the analytic solution of the model was derived. The simulation results indicate that the evolution time of social network and the biggest social hierarchy gap among individuals may be the origin which results in the power-law-like degree distribution diversity. Therefore, the model is helpful to comprehend the law of human social interaction.

Suggested Citation

  • Xu, Xiao-Ting & Wang, Nianxin & Bian, Jun & Zhou, Bin, 2019. "Understanding the diversity on power-law-like degree distribution in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 576-581.
    • Handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:576-581
    DOI: 10.1016/j.physa.2019.03.104
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    References listed on IDEAS

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    Cited by:

    1. Yang, Qing-Lin & Wang, Li-Fu & Zhao, Guo-Tao & Guo, Ge, 2020. "A coarse graining algorithm based on m-order degree in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    2. Gao, Shilong & Gao, Nunan & Kan, Bixia & Wang, Huiqi, 2021. "Stochastic resonance in coupled star-networks with power-law heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    3. Reyes-Santias, Francisco & Reboredo, Juan C. & de Assis, Edilson Machado & Rivera-Castro, Miguel A., 2021. "Does length of hospital stay reflect power-law behavior? A q-Weibull density approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).

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