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Complex Networks from Chaotic Time Series on Riemannian Manifold |
| Jian-Cheng Sun** |
School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013
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| Cite this article: |
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Jian-Cheng Sun 2016 Chin. Phys. Lett. 33 100503 |
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Abstract Complex networks are important paradigms for analyzing the complex systems as they allow understanding the structural properties of systems composed of different interacting entities. In this work we propose a reliable method for constructing complex networks from chaotic time series. We first estimate the covariance matrices, then a geodesic-based distance between the covariance matrices is introduced. Consequently the network can be constructed on a Riemannian manifold where the nodes and edges correspond to the covariance matrix and geodesic-based distance, respectively. The proposed method provides us with an intrinsic geometry viewpoint to understand the time series.
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Received: 12 July 2016
Published: 27 October 2016
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| PACS: |
05.45.-a
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(Nonlinear dynamics and chaos)
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05.10.-a
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(Computational methods in statistical physics and nonlinear dynamics)
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05.10.Gg
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(Stochastic analysis methods)
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| Fund: Supported by the National Natural Science Foundation of China under Grant No 61362024. |
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