| 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. |
