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Europhysics Letters, 19(5), 50008 (2017) DOI:10.1209/0295-5075/119/50008
Reconstructing multi-mode networks from multivariate time series
Z. Gao, Y. Yang, W. Dang, Q. Cai, Z. Wang, N. Marwan, S. Boccaletti, J. KurthsUnveiling the dynamics hidden in multivariate time series is a task of the utmost importance in a broad variety of areas in physics. We here propose a method that leads to the construction of a novel functional network, a multi-mode weighted graph combined with an empirical mode decomposition, and to the realization of multi-information fusion of multivariate time series. The method is illustrated in a couple of successful applications (a multi-phase flow and an epileptic electro-encephalogram), which demonstrate its powerfulness in revealing the dynamical behaviors underlying the transitions of different flow patterns, and enabling to differentiate brain states of seizure and non-seizure.
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