Antonopoulos, Chris (2023) Network inference combining mutual information rate and statistical tests. Communications in Nonlinear Science and Numerical Simulation, 116. p. 106896. DOI https://doi.org/10.1016/j.cnsns.2022.106896
Antonopoulos, Chris (2023) Network inference combining mutual information rate and statistical tests. Communications in Nonlinear Science and Numerical Simulation, 116. p. 106896. DOI https://doi.org/10.1016/j.cnsns.2022.106896
Antonopoulos, Chris (2023) Network inference combining mutual information rate and statistical tests. Communications in Nonlinear Science and Numerical Simulation, 116. p. 106896. DOI https://doi.org/10.1016/j.cnsns.2022.106896
Abstract
In this paper, we present a method that combines information-theoretical and statistical approaches to infer connec- tivity in complex networks using time-series data. The method is based on estimations of the Mutual Information Rate for pairs of time-series and on statistical significance tests for connectivity acceptance using the false discovery rate method for multiple hypothesis testing. We provide the mathematical background on Mutual Information Rate, discuss the statistical significance tests and the false discovery rate. Further on, we present results for corre- lated normal-variates data, coupled circle and coupled logistic maps, coupled Lorenz systems and coupled stochastic Kuramoto phase oscillators. Following up, we study the effect of noise on the presented methodology in networks of coupled stochastic Kuramoto phase oscillators and of coupling heterogeneity degree on networks of coupled circle maps. We show that the method can infer the correct number and pairs of connected nodes, by means of receiver operating characteristic curves. In the more realistic case of stochastic data, we demonstrate its ability to infer the structure of the initial connectivity matrices. The method is also shown to recover the initial connectivity matrices for dynamics on the nodes of Erd ̋os-R ́enyi and small-world networks with varying coupling heterogeneity in their connections. The highlight of the proposed methodology is its ability to infer the underlying network connectivity based solely on the recorded datasets.
Item Type: | Article |
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Additional Information: | 25 pages, 7 figures |
Uncontrolled Keywords: | Network inference; Mutual information rate; Mutual information; Statistical tests; False discovery rate; Shannon entropy; Complex systems; Complex networks; Time-series data |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 06 Oct 2022 16:20 |
Last Modified: | 30 Oct 2024 20:52 |
URI: | http://repository.essex.ac.uk/id/eprint/33489 |
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