Lyapunov Exponents for Temporal Networks
Data Analysis, Statistics and Probability
2023-05-03 v1 Chaotic Dynamics
Abstract
By interpreting a temporal network as a trajectory of a latent graph dynamical system, we introduce the concept of dynamical instability of a temporal network, and construct a measure to estimate the network Maximum Lyapunov Exponent (nMLE) of a temporal network trajectory. Extending conventional algorithmic methods from nonlinear time-series analysis to networks, we show how to quantify sensitive dependence on initial conditions, and estimate the nMLE directly from a single network trajectory. We validate our method for a range of synthetic generative network models displaying low and high dimensional chaos, and finally discuss potential applications.
Keywords
Cite
@article{arxiv.2301.12966,
title = {Lyapunov Exponents for Temporal Networks},
author = {Annalisa Caligiuri and Victor M. Eguiluz and Leonardo di Gaetano and Tobias Galla and Lucas Lacasa},
journal= {arXiv preprint arXiv:2301.12966},
year = {2023}
}