On the spectral properties of Feigenbaum graphs

Abstract

A horizontal visibility graph (HVG) is a simple graph extracted from an ordered sequence of real values, and this mapping has been used to provide a combinatorial encoding of time series for the task of performing network based time series analysis. While some properties of the spectrum of these graphs—such as the largest eigenvalue of the adjacency matrix—have been routinely used as measures to characterise time series complexity, a theoretic understanding of such properties is lacking. In this work we explore some algebraic and spectral properties of these graphs associated to periodic and chaotic time series. We focus on the family of Feigenbaum graphs, which are HVGs constructed in correspondence with the trajectories of one-parameter unimodal maps undergoing a period-doubling route to chaos (Feigenbaum scenario). For the set of values of the map’s parameter for which the orbits are periodic with period 2n, Feigenbaum graphs are fully characterised by two integers and admit an algebraic structure. We explore the spectral properties of these graphs for finite n and k, and among other interesting patterns we find a scaling relation for the maximal eigenvalue and we prove some bounds explaining it. We also provide numerical and rigorous results on a few other properties including the determinant or the number of spanning trees. In a second step, we explore the set of Feigenbaum graphs obtained for the range of values of the map’s parameter for which the system displays chaos. We show that in this case, Feigenbaum graphs form an ensemble for each value of and the system is typically weakly self-averaging. Unexpectedly, we find that while the largest eigenvalue can easily discriminate chaos from an uncorrelated (white) stochastic process, it is not a good measure to quantify the chaoticity of the process, and that the eigenvalue density does a better job.