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Multifractality and Network Analysis of Phase Transition

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  • Longfeng Zhao
  • Wei Li
  • Chunbin Yang
  • Jihui Han
  • Zhu Su
  • Yijiang Zou

Abstract

Many models and real complex systems possess critical thresholds at which the systems shift dramatically from one sate to another. The discovery of early-warnings in the vicinity of critical points are of great importance to estimate how far the systems are away from the critical states. Multifractal Detrended Fluctuation analysis (MF-DFA) and visibility graph method have been employed to investigate the multifractal and geometrical properties of the magnetization time series of the two-dimensional Ising model. Multifractality of the time series near the critical point has been uncovered from the generalized Hurst exponents and singularity spectrum. Both long-term correlation and broad probability density function are identified to be the sources of multifractality. Heterogeneous nature of the networks constructed from magnetization time series have validated the fractal properties. Evolution of the topological quantities of the visibility graph, along with the variation of multifractality, serve as new early-warnings of phase transition. Those methods and results may provide new insights about the analysis of phase transition problems and can be used as early-warnings for a variety of complex systems.

Suggested Citation

  • Longfeng Zhao & Wei Li & Chunbin Yang & Jihui Han & Zhu Su & Yijiang Zou, 2017. "Multifractality and Network Analysis of Phase Transition," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.
    • Handle: RePEc:plo:pone00:0170467
    DOI: 10.1371/journal.pone.0170467
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    References listed on IDEAS

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    1. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    2. Wei-Xing Zhou, 2008. "Multifractal detrended cross-correlation analysis for two nonstationary signals," Papers 0803.2773, arXiv.org.
    3. Kantelhardt, Jan W & Koscielny-Bunde, Eva & Rego, Henio H.A & Havlin, Shlomo & Bunde, Armin, 2001. "Detecting long-range correlations with detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 295(3), pages 441-454.
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    1. Longfeng Zhao & Wei Li & Andrea Fenu & Boris Podobnik & Yougui Wang & H. Eugene Stanley, 2017. "The q-dependent detrended cross-correlation analysis of stock market," Papers 1705.01406, arXiv.org, revised Jun 2017.
    2. Gulich, Damián & Baglietto, Gabriel & Rozenfeld, Alejandro F., 2018. "Temporal correlations in the Vicsek model with vectorial noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 590-604.
    3. An, Sufang & An, Feng & Gao, Xiangyun & Wang, Anjian, 2023. "Early warning of critical transitions in crude oil price," Energy, Elsevier, vol. 280(C).
    4. Charutha, S. & Gopal Krishna, M. & Manimaran, P., 2020. "Multifractal analysis of Indian public sector enterprises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    5. Shao, Wei & Wang, Jian, 2020. "Does the “ice-breaking” of South and North Korea affect the South Korean financial market?," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    6. Mondal, Mitali & Mondal, Arindam & Mondal, Joyati & Patra, Kanchan Kumar & Deb, Argha & Ghosh, Dipak, 2018. "Evidence of centrality dependent fractal behavior in high energy heavy ion interactions: Hint of two different sources," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 230-237.
    7. Peng, Xiaoyi & Zhao, Yi & Small, Michael, 2020. "Identification and prediction of bifurcation tipping points using complex networks based on quasi-isometric mapping," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).

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