Asymmetrically Weighted Dowker Persistence and Applications in Dynamical Systems

TL;DR

This paper introduces an asymmetric weighted Dowker persistence framework that captures temporal and spatial information in dynamical systems, improving differentiation of periodic and non-periodic cycles.

Contribution

It presents a novel method combining weighted directed networks and Dowker homology to analyze dynamical data, addressing high dimensionality and directional information challenges.

Findings

Characterizes differences in periodic and non-periodic cycles via Dowker persistence.

Proves homology descriptions for graph wedge sums and cactus graphs.

Provides a noise-robust, temporally sensitive topological analysis method.

Abstract

By their nature it is difficult to differentiate chaotic dynamical systems through measurement. In recent years, work has begun on using methods of Topological Data Analysis (TDA) to qualitatively type dynamical data by approximating the topology of the underlying attracting set. This comes with the additional challenges of high dimensionality incurring computational complexity along with the lack of directional information encoded in the approximated topology. Due to the latter fact, standard methods of TDA for this high dimensional dynamical data do not differentiate between periodic cycles and non-periodic cycles in the attractor. We present a framework to address both of these challenges. We begin by binning the dynamical data, and capturing the sequential information in the form of a coarse-grained weighted and directed network. We then calculate the persistent Dowker homology of the asymmetric network, encoding spatial and temporal information. Analytically, we highlight the differences in periodic and non-periodic cycles by providing a full characterization of their one-dimensional Dowker persistences. We prove how the homologies of graph wedge sums can be described in terms of the wedge component homologies. Finally, we generalize our characterization to cactus graphs with arbitrary edge weights and orientations. Our analytical results give insight into how our method captures temporal information in its asymmetry, producing a persistence framework robust to noise and sensitive to dynamical structure.