Kaki Bardia, Farhang Nastaran, Safari Hossein
Department of Physics, Faculty of Science, University of Zanjan, P.O. Box 45195-313, Zanjan, Iran.
Department of Physics, Isfahan University of Technology, P.O. Box 84156-83111, Isfahan, Iran.
Sci Rep. 2022 Oct 7;12(1):16835. doi: 10.1038/s41598-022-20473-4.
Determination of self-organized criticality (SOC) is crucial in evaluating the dynamical behavior of a time series. Here, we apply the complex network approach to assess the SOC characteristics in synthesis and real-world data sets. For this purpose, we employ the horizontal visibility graph (HVG) method and construct the relevant networks for two numerical avalanche-based samples (i.e., sand-pile models), several financial markets, and a solar nano-flare emission model. These series are shown to have long-temporal correlations via the detrended fluctuation analysis. We compute the degree distribution, maximum eigenvalue, and average clustering coefficient of the constructed HVGs and compare them with the values obtained for random and chaotic processes. The results manifest a perceptible deviation between these parameters in random and SOC time series. We conclude that the mentioned HVG's features can distinguish between SOC and random systems.
自组织临界性（SOC）的确定对于评估时间序列的动态行为至关重要。在此，我们应用复杂网络方法来评估合成数据集和真实世界数据集中的SOC特征。为此，我们采用水平可见性图（HVG）方法，并为两个基于数值雪崩的样本（即沙堆模型）、几个金融市场和一个太阳纳米耀斑发射模型构建相关网络。通过去趋势波动分析表明，这些序列具有长期相关性。我们计算所构建HVG的度分布、最大特征值和平均聚类系数，并将它们与随机过程和混沌过程所获得的值进行比较。结果表明，随机时间序列和SOC时间序列在这些参数上存在明显差异。我们得出结论，上述HVG的特征可以区分SOC系统和随机系统。
