Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 390, Issue 20, Pages 3592-3601Publisher
ELSEVIER
DOI: 10.1016/j.physa.2011.04.020
Keywords
Horizontal visibility graph; Fractional Brownian motion; Dynamics; Fractality; Mixing pattern
Categories
Funding
- Shanghai Rising Star Program [11QH1400800]
- National Natural Science Foundation of China [11075054]
- Fundamental Research Funds for the Central Universities
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Nonlinear time series analysis aims at understanding the dynamics of stochastic or chaotic processes. In recent years, quite a few methods have been proposed to transform a single time series to a complex network so that the dynamics of the process can be understood by investigating the topological properties of the network. We study the topological properties of horizontal visibility graphs constructed from fractional Brownian motions with different Hurst indexes H epsilon (0. 1). Special attention has been paid to the impact of the Hurst index on topological properties. It is found that the clustering coefficient C decreases when H increases. We also found that the mean length L of the shortest paths increases exponentially with H for fixed length N of the original time series. In addition, L increases linearly with respect to N when H is close to 1 and in a logarithmic form when H is close to 0. Although the occurrence of different motifs changes with H, the motif rank pattern remains unchanged for different H. Adopting the node-covering box-counting method, the horizontal visibility graphs are found to be fractals and the fractal dimension dB decreases with H. Furthermore, the Pearson coefficients of the networks are positive and the degree-degree correlations increase with degree, which indicate that the horizontal visibility graphs are assortative. With the increase of H, the Pearson coefficient decreases first and then increases, in which the turning point is around H = 0.6. The presence of both fractality and assortativity in the horizontal visibility graphs converted from fractional Brownian motions is different from many cases where fractal networks are usually disassortative. (c) 2011 Elsevier B.V. All rights reserved.
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