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Degree distributions and motif profiles of limited penetrable horizontal visibility graphs

Author

Listed:
  • Wang, Minggang
  • Xu, Hua
  • Tian, Lixin
  • Eugene Stanley, H.

Abstract

The algorithm of limited penetrable horizontal visibility graphs (LPHVGs) including the limited penetrable horizontal visibility graph [LPHVG(ρ)], the directed limited penetrable horizontal visibility graph [DLPHVG(ρ)] and the image limited penetrable horizontal visibility graph [ILPHVGn(ρ))] are used to map time series (or matrices) on graphs and are powerful tools for analyzing time series. We derive the degree distributions of LPHVGs using an iterative LPHVGs construction process. We propose a more intuitive method of reproducing the construction process of limited penetrable horizontal visibility graphs that is simple to calculate. We find that the results confirm the analytical results from previous methods. We then introduce the concept of sequential LPHVG(ρ) motifs and present a theoretical way of computing the exact motif profiles associated with unrelated random series. We perform several numerical simulations to further check the accuracy of our theoretical results. Finally we use the analytical results of LPHVG(ρ) motif profiles to distinguish among random, periodic, and chaotic signals and find that the frequency of the type-I motif captures sufficient information to easily distinguish among different processes.

Suggested Citation

  • Wang, Minggang & Xu, Hua & Tian, Lixin & Eugene Stanley, H., 2018. "Degree distributions and motif profiles of limited penetrable horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 620-634.
    • Handle: RePEc:eee:phsmap:v:509:y:2018:i:c:p:620-634
    DOI: 10.1016/j.physa.2018.06.077
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    References listed on IDEAS

    as
    1. M. E. J. Newman & D. J. Watts, 1999. "Renormalization Group Analysis of the Small-World Network Model," Working Papers 99-04-029, Santa Fe Institute.
    2. Xiao, Jiang & Wang, Minggang & Tian, Lixin & Zhen, Zaili, 2018. "The measurement of China’s consumer market development based on CPI data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 664-680.
    3. Gang-Jin Wang & Chi Xie & Shou Chen, 2017. "Multiscale correlation networks analysis of the US stock market: a wavelet analysis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 561-594, October.
    4. An, Haizhong & Gao, Xiangyun & Fang, Wei & Ding, Yinghui & Zhong, Weiqiong, 2014. "Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: A complex network approach," Applied Energy, Elsevier, vol. 136(C), pages 1067-1075.
    5. Zhen Zhang & Minggang Wang & Lixin Tian & Wenbin Zhang, 2017. "Research on the development efficiency of regional high-end talent in China: A complex network approach," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-18, December.
    6. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    7. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    8. Wang, Minggang & Chen, Ying & Tian, Lixin & Jiang, Shumin & Tian, Zihao & Du, Ruijin, 2016. "Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective," Applied Energy, Elsevier, vol. 175(C), pages 109-127.
    9. Zhang, Zhen & Wang, Minggang & Xu, Hua & Zhang, Wenbin & Tian, Lixin, 2018. "Research on the co-movement between high-end talent and economic growth: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1216-1225.
    10. Wang, Minggang & Tian, Lixin, 2016. "From time series to complex networks: The phase space coarse graining," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 456-468.
    11. Wang, Minggang & Tian, Lixin & Xu, Hua & Li, Weiyu & Du, Ruijin & Dong, Gaogao & Wang, Jie & Gu, Jiani, 2017. "Systemic risk and spatiotemporal dynamics of the consumer market of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 188-204.
    12. Wang, Minggang & Tian, Lixin & Du, Ruijin, 2016. "Research on the interaction patterns among the global crude oil import dependency countries: A complex network approach," Applied Energy, Elsevier, vol. 180(C), pages 779-791.
    13. Huan Chen & Lixin Tian & Minggang Wang & Zaili Zhen, 2017. "Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks," Sustainability, MDPI, vol. 9(4), pages 1-29, April.
    14. Gang-Jin Wang & Chi Xie & H. Eugene Stanley, 2018. "Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 607-635, March.
    15. Wang, Jiang & Yang, Chen & Wang, Ruofan & Yu, Haitao & Cao, Yibin & Liu, Jing, 2016. "Functional brain networks in Alzheimer’s disease: EEG analysis based on limited penetrable visibility graph and phase space method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 174-187.
    16. Du, Ruijin & Wang, Ya & Dong, Gaogao & Tian, Lixin & Liu, Yixiao & Wang, Minggang & Fang, Guochang, 2017. "A complex network perspective on interrelations and evolution features of international oil trade, 2002–2013," Applied Energy, Elsevier, vol. 196(C), pages 142-151.
    17. Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
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