A Dynamical Approach to Efficient Eigenvalue Estimation in General Multiagent Networks

TL;DR

This paper introduces a dynamical method for efficiently estimating eigenvalues of complex multiagent networks from limited observational data without prior knowledge of agent contributions.

Contribution

It presents a novel algorithm that exactly recovers the observable eigenvalues of arbitrary multiagent networks using minimal, finite-time measurements, applicable to both continuous and discrete systems.

Findings

Exact eigenvalue recovery with at most twice the number of agents in measurements

Applicable to weighted, directed, and arbitrary dynamical networks

Validated through numerical simulations

Abstract

We propose a method to efficiently estimate the eigenvalues of any arbitrary (potentially weighted and/or directed) network of interacting dynamical agents from dynamical observations. These observations are discrete, temporal measurements about the evolution of the outputs of a subset of agents (potentially one) during a finite time horizon; notably, we do not require knowledge of which agents are contributing to our measurements. We propose an efficient algorithm to exactly recover the (potentially complex) eigenvalues corresponding to network modes that are observable from the output measurements. The length of the sequence of measurements required by our method to generate a full reconstruction of the observable eigenvalue spectrum is, at most, twice the number of agents in the network, but smaller in practice. The proposed technique can be applied to networks of multiagent systems with arbitrary dynamics in both continuous- and discrete-time. Finally, we illustrate our results with numerical simulations.