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Evolving hypernetwork model

Author

Listed:
  • Jian-Wei Wang
  • Li-Li Rong
  • Qiu-Hong Deng
  • Ji-Yong Zhang

Abstract

Complex hypernetworks are ubiquitous in real-life systems. While a substantial body of previous research has only focused on the applications of hypernetworks, relatively little work has investigated the evolving models of hypernetworks. Considering the formations of many real world networks, we propose two evolving mechanisms of the hyperedge growth and the hyperedge preferential attachment, then construct an evolving hypernetwork model. We introduce some basic topological quantities, such as a variety of degree distributions, clustering coefficients as well as average path length. We numerically investigate these quantities in the limit of large hypernetwork size and find that our hypernetwork model shares similar qualitative features with the majority of complex networks that have been previously studied, such as the scale-free property of the degree distribution and a high degree of clustering, as well as the small-world property. It is expected that our attempt in the hypernetwork model can bring the upsurge in the study of the hypernetwork model in further. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010

Suggested Citation

  • Jian-Wei Wang & Li-Li Rong & Qiu-Hong Deng & Ji-Yong Zhang, 2010. "Evolving hypernetwork model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 493-498, October.
    • Handle: RePEc:spr:eurphb:v:77:y:2010:i:4:p:493-498
    DOI: 10.1140/epjb/e2010-00297-8
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    Citations

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    Cited by:

    1. Liu, Zhong & Huang, Jincai & Cheng, Guangquan, 2016. "Community detection in hypernetwork via Density-Ordered Tree partitionAuthor-Name: Cheng, Qing," Applied Mathematics and Computation, Elsevier, vol. 276(C), pages 384-393.
    2. Yang, Guang-Yong & Hu, Zhao-Long & Liu, Jian-Guo, 2015. "Knowledge diffusion in the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 429-436.
    3. Xuwei Pan & Shenglan He & Xiyong Zhu & Qingmiao Fu, 2016. "How users employ various popular tags to annotate resources in social tagging: An empirical study," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1121-1137, May.
    4. Zhou, Zhidong & Jin, Zhen & Jin, Jun & Song, Haitao, 2020. "Emergence of scaling in evolving hypernetworks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 546(C).
    5. Wang, Jiang-Pan & Guo, Qiang & Yang, Guang-Yong & Liu, Jian-Guo, 2015. "Improved knowledge diffusion model based on the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 250-256.
    6. Jian-Guo Liu & Guang-Yong Yang & Zhao-Long Hu, 2014. "A Knowledge Generation Model via the Hypernetwork," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-8, March.
    7. Shen, Ai-Zhong & Guo, Jin-Li & Suo, Qi, 2017. "Study of the variable growth hypernetworks influence on the scaling law," Chaos, Solitons & Fractals, Elsevier, vol. 97(C), pages 84-89.
    8. Qiao, Jian & Meng, Ying-Ying & Chen, Hsinchun & Huang, Hong-Qiao & Li, Guo-Ying, 2016. "Modeling one-mode projection of bipartite networks by tagging vertex information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 270-279.
    9. Sun, Yunpeng & Jia, Ruoya & Razzaq, Asif & Bao, Qun, 2024. "Social network platforms and climate change in China: Evidence from TikTok," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    10. Shen, Ai-Zhong & Guo, Jin-Li & Wu, Guo-Lin & Jia, Shu-Wei, 2018. "The agglomeration phenomenon influence on the scaling law of the scientific collaboration system," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 461-467.
    11. Wang, Zhiping & Yin, Haofei & Jiang, Xin, 2020. "Exploring the dynamic growth mechanism of social networks using evolutionary hypergraph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 544(C).
    12. Ma, Xiujuan & Ma, Fuxiang & Yin, Jun & Zhao, Haixing, 2018. "Cascading failures of k uniform hyper-network based on the hyper adjacent matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 281-289.
    13. Li, Shuyu & Li, Xiang, 2023. "Influence maximization in hypergraphs: A self-optimizing algorithm based on electrostatic field," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    14. 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.
    15. Suo, Qi & Guo, Jin-Li & Sun, Shiwei & Liu, Han, 2018. "Exploring the evolutionary mechanism of complex supply chain systems using evolving hypergraphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 141-148.
    16. Feng Hu & Lin Ma & Xiu-Xiu Zhan & Yinzuo Zhou & Chuang Liu & Haixing Zhao & Zi-Ke Zhang, 2021. "The aging effect in evolving scientific citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4297-4309, May.

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