title: Successful network inference from time-series data using mutual information rate.
abstract: 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.
authors.affiliation: Institute for Complex Systems and Mathematical Biology, SUPA, University of Aberdeen, King's College, AB24 3UE Aberdeen, United Kingdom.
authors.forename: E
authors.initials: E
authors.lastname: Bianco-Martinez
authors.affiliation: Institute for Complex Systems and Mathematical Biology, SUPA, University of Aberdeen, King's College, AB24 3UE Aberdeen, United Kingdom.
authors.forename: N
authors.initials: N
authors.lastname: Rubido
authors.affiliation: Department of Mathematical Sciences, University of Essex, Wivenhoe Park, CO4 3SQ Colchester, United Kingdom.
authors.forename: Ch G
authors.initials: ChG
authors.lastname: Antonopoulos
authors.affiliation: Institute for Complex Systems and Mathematical Biology, SUPA, University of Aberdeen, King's College, AB24 3UE Aberdeen, United Kingdom.
authors.forename: M S
authors.initials: MS
authors.lastname: Baptista