Statistical properties of visibility graph of energy dissipation rates in three-dimensional fully developed turbulence
arXiv:0905.1831 · doi:10.1016/j.physa.2010.02.043
Abstract
We study the statistical properties of the network constructed from the energy dissipation rate time series in the three dimensional developed turbulence using the visibility algorithm. The degree distribution is found to have a power-law tail with the tail exponent . The exponential relationship between the number of the boxes and the box size in the edge-covering box-counting method illustrates that the network is not self-similar, which is also confirmed by the hub-hub attraction according to the visibility algorithm. In addition, it is found that the skeleton of the visibility network exhibits excellent allometric scaling with the scaling exponent .
7 pages, 5 figures
References in corpus (12)
- Power-law distributions in empirical data
- From time series to complex networks: the visibility graph
- The multifractal nature of turbulent energy dissipation
- Recurrence networks - A novel paradigm for nonlinear time series analysis
- Complex Network Approach for Recurrence Analysis of Time Series
- Scale-free trees: the skeletons of complex networks
- Degree distribution of the visibility graphs mapped from fractional Brownian motions and multifractal random walks
- Universal and nonuniversal allometric scaling behaviors in the visibility graphs of world stock market indices
- Scaling of degree correlations and the influence on diffusion in scale-free networks
- Exploring self-similarity of complex cellular networks: The edge-covering method with simulated annealing and log-periodic sampling
- Statistical properties of world investment networks
- Superfamily classification of nonstationary time series based on DFA scaling exponents
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