Exact Network Reconstruction from Consensus Signals and One Eigenvalue

TL;DR

This paper presents a method to exactly reconstruct unknown network topologies using only one eigenvalue and high-frequency noise, improving robustness and simplicity over previous techniques.

Contribution

It combines eigenvalue spectrum estimation with consensus signal noise to achieve exact network reconstruction from minimal spectral information.

Findings

Numerical simulations confirm exact graph reconstruction.

High-frequency noise facilitates easy filtering of signals.

Method is robust across various network topologies.

Abstract

The basic inverse problem in spectral graph theory consists in determining the graph given its eigenvalue spectrum. In this paper, we are interested in a network of technological agents whose graph is unknown, communicating by means of a consensus protocol. Recently, the use of artificial noise added to consensus signals has been proposed to reconstruct the unknown graph, although errors are possible. On the other hand, some methodologies have been devised to estimate the eigenvalue spectrum, but noise could interfere with the elaborations. We combine these two techniques in order to simplify calculations and avoid topological reconstruction errors, using only one eigenvalue. Moreover, we use an high frequency noise to reconstruct the network, thus it is easy to filter the control signals after the graph identification. Numerical simulations of several topologies show an exact and robust reconstruction of the graphs.