Mixed measurements and the detection of phase synchronization in networks

TL;DR

This paper investigates how using mixed measurements, including variables with poor observability, affects the effectiveness of multivariate singular spectrum analysis (M-SSA) in detecting phase synchronization in networks of nonlinear oscillators.

Contribution

It provides a numerical analysis of the impact of mixed measurement variables on M-SSA's ability to detect phase synchronization, highlighting limitations and potential strategies.

Findings

Poorly observable variables hinder node detection in M-SSA.

Using poor variables slightly impairs synchronization clustering detection.

Global phase synchronization can still be identified with enough poor variables.

Abstract

Multivariate singular spectrum analysis (M-SSA), with a varimax rotation of eigenvectors, was recently proposed to provide detailed information about phase synchronization in networks of nonlinear oscillators without any a priori need for phase estimation. The discriminatory power of M-SSA is often enhanced by using only the time series of the variable that provides the best observability of the node dynamics. In practice, however, diverse factors could prevent one to have access to this variable in some nodes and other variables should be used, resulting in a mixed set of variables. In the present work, the impact of this mixed measurement approach on the M-SSA is numerically investigated in networks of R\"ossler systems and cord oscillators. The results are threefold. First, a node measured by a poor variable, in terms of observability, becomes virtually invisible to the technique. Second, a side effect of using a poor variable is that the characterization of phase synchronization clustering of the {\it other}\, nodes is hindered by a small amount. This suggests that, given a network, synchronization analysis with M-SSA could be more reliable by not measuring those nodes that are accessible only through poor variables. Third, global phase synchronization could be detected even using only poor variables, given enough of them are measured. These insights could be useful in defining measurement strategies for both experimental design and real world applications for use with M-SSA.