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Successful network inference from time-series data using Mutual Information Rate

Bianco-Martinez, E and Rubido, N and Antonopoulos, CG and Baptista, MS (2016) 'Successful network inference from time-series data using Mutual Information Rate.' Chaos: an interdisciplinary journal of nonlinear science, 26 (4). 043102-043102. ISSN 1054-1500

1603.05825v1.pdf - Accepted Version

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This work uses an information-based methodology to infer the connectivity of complex systems from observed time-series data. We first derive analytically an expression for the Mutual Information Rate (MIR), namely, the amount of information exchanged per unit of time, that can be used to estimate the MIR between two finite-length low-resolution noisy time-series, and then apply it after a proper normalization for the identification of the connectivity structure of small networks of interacting dynamical systems. In particular, we show that our methodology successfully infers the connectivity for heterogeneous networks, different time-series lengths or coupling strengths, and even in the presence of additive noise. Finally, we show that our methodology based on MIR successfully infers the connectivity of networks composed of nodes with different time-scale dynamics, where inference based on Mutual Information fails.

Item Type: Article
Uncontrolled Keywords: nlin.CD
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 16 Jun 2016 09:12
Last Modified: 15 Jan 2022 00:43

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