Work

Chaos-induced hindrance of connectivity detection and topological unpredictability

Year: 2025

Type: article

Abstract: The experimental assessment of networks within a complex system requires the inference of links. A common way to detect a link relies on the assumption that time series recorded out of two nodes contain sufficient shared information so as to detect a correlation, using either linear measures or more sensitive information-theoretical tools. While this assumption is theoretically granted for any system described by deterministically coupled differential equations , in an experimental scenario the emergence of chaotic regimes can hinder its validity. In this work we explore the issue of assessing connectivity in the prototypical case of a linear network of nonlinear oscillators, experimentally-implemented via a scalable electronic analog circuitry. Despite strong coupling, the assessed connectivity strength decays for an increasing length of the network. This phenomenon, which is interpreted in terms of a “topological unpredictability” of chaos, eventually leads to an apparent lack of connection between nodes that, in reality, are physically coupled. Our results provide insights on the difficulty of inferring links out of time series, with implications in the identification of networks in real systems, for example in Earth science and neuroscience .

Source: Chaos Solitons & Fractals

DOI: 10.1016/j.chaos.2025.116637

Authors: Alessio Perinelli, Matteo Cescato, Roberto Iuppa, Leonardo Ricci

Institutions: Istituto Nazionale di Fisica Nucleare, University of Trento, Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics And Applications

Language: English

Topic: Chaos control and synchronization

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,970

Funder: Agenzia Spaziale Italiana