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Hypernetwork models based on random hypergraphs

Author

Listed:
  • Feng Hu

    (School of Computer Science, Qinghai Normal University, Xining, Qinghai 810008, P. R. China2Key Laboratory of Tibetan Information Processing and Machine Translation, Qinghai Province, Xining, Qinghai 810008, P. R. China)

  • Jin-Li Guo

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • Fa-Xu Li

    (School of Computer Science, Qinghai Normal University, Xining, Qinghai 810008, P. R. China2Key Laboratory of Tibetan Information Processing and Machine Translation, Qinghai Province, Xining, Qinghai 810008, P. R. China)

  • Hai-Xing Zhao

    (School of Computer Science, Qinghai Normal University, Xining, Qinghai 810008, P. R. China2Key Laboratory of Tibetan Information Processing and Machine Translation, Qinghai Province, Xining, Qinghai 810008, P. R. China)

Abstract

Hypernetworks are ubiquitous in real-world systems. They provide a powerful means of accurately depicting networks of different types of entity and will attract more attention from researchers in the future. Most previous hypernetwork research has been focused on the application and modeling of uniform hypernetworks, which are based on uniform hypergraphs. However, random hypernetworks are generally more common, therefore, it is useful to investigate the evolution mechanisms of random hypernetworks. In this paper, we construct three dynamic evolutional models of hypernetworks, namely the equal-probability random hypernetwork model, the Poisson-probability random hypernetwork model and the certain-probability random hypernetwork model. Furthermore, we analyze the hyperdegree distributions of the three models with mean-field theory, and we simulate each model numerically with different parameter values. The simulation results agree well with the results of our theoretical analysis, and the findings indicate that our models could help understand the structure and evolution mechanisms of real systems.

Suggested Citation

  • Feng Hu & Jin-Li Guo & Fa-Xu Li & Hai-Xing Zhao, 2019. "Hypernetwork models based on random hypergraphs," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 30(08), pages 1-15, August.
    • Handle: RePEc:wsi:ijmpcx:v:30:y:2019:i:08:n:s0129183119500529
    DOI: 10.1142/S0129183119500529
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    Cited by:

    1. Zhu, Mixin & Zhou, Xiaojun, 2022. "Hypergraph-based joint optimization of spare part provision and maintenance scheduling for serial-parallel multi-station manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Linzenich, Anika & Arning, Katrin & Bongartz, Dominik & Mitsos, Alexander & Ziefle, Martina, 2019. "What fuels the adoption of alternative fuels? Examining preferences of German car drivers for fuel innovations," Applied Energy, Elsevier, vol. 249(C), pages 222-236.

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