Characterizing the complexity of time series network graphs: A simplicial approach

TL;DR

This paper introduces a novel simplicial approach using algebraic topology to analyze the complexity of time series networks derived from the logistic map, effectively distinguishing different dynamical regimes.

Contribution

It develops simplicial characterizers that reveal hierarchical topological complexity and local dynamics, outperforming conventional network measures in regime differentiation.

Findings

Simplicial characterizers distinguish between periodic, chaotic, and intermittent regimes.

Hierarchical topological levels reflect local dynamics influencing global behavior.

Combined use of simplicial and conventional measures enhances dynamical analysis.

Abstract

We analyze the time series obtained from different dynamical regimes of the logistic map by constructing their equivalent time series (TS) networks, using the visibility algorithm. The regimes analyzed include both periodic and chaotic regimes, as well as intermittent regimes and the Feigenbaum attractor at the edge of chaos. We use the methods of algebraic topology to define the simplicial characterizers, which can analyse the simplicial structure of the networks at both the global and local levels. The simplicial characterisers bring out the hierarchical levels of complexity at various topological levels. These hierarchical levels of complexity find the skeleton of the local dynamics embedded in the network which influence the global dynamical properties of the system, and also permit the identification of dominant motifs. We also analyze the same networks using conventional network characterizers such as average path lengths and clustering coefficients. We see that the simplicial characterizers are capable of distinguishing between different dynamical regimes and can pick up subtle differences in dynamical behavior, whereas the usual characterizers provide a coarser characterization. However, the two taken in conjunction, can provide information about the dynamical behavior of the time series, as well as the correlations in the evolving system. Our methods can therefore provide powerful tools for the analysis of dynamical systems.