Exact Network Reconstruction from Consensus Signals and One Eigenvalue

Fioriti, Enzo; Chiesa, Stefano Della; Fratichini, Fabio

doi:10.48550/arxiv.1308.3775

Cited by 1 publication

(2 citation statements)

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“…In this article we present an algorithm which can infer the undirected graph of interaction among the agents undergoing consensus dynamics from the measured time series of output of each agent. Notable works in the direction of topology learning from time series measurements of linear dynamical systems are [9], [10], [11], [12], [13], [14], [15], [16]. [11] and [12] adopt an active approach of inferring the topology by removing one node from the network at a time (known as node knockout).…”

Section: Introductionmentioning

confidence: 99%

“…However, no analytical guarantees on the accuracy of the reconstruction are provided. [14], [15], [16] present topology learning algorithms for undirected consensus networks which utilize some knowledge of system parameters and also assume white noise model for the receiver noise. The framework presented here is not limited to a white noise model of receiver noise.…”

Section: Introductionmentioning

confidence: 99%

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Learning the exact topology of undirected consensus networks

Talukdar

Deka

Attree

et al. 2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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In this article, we present a method to learn the interaction topology of a network of agents undergoing linear consensus updates in a non invasive manner. Our approach is based on multivariate Wiener filtering, which is known to recover spurious edges apart from the true edges in the topology. The main contribution of this work is to show that in the case of undirected consensus networks, all spurious links obtained using Wiener filtering can be identified using frequency response of the Wiener filters. Thus, the exact interaction topology of the agents is unveiled. The method presented requires time series measurements of the state of the agents and does not require any knowledge of link weights. To the best of our knowledge this is the first approach that provably reconstructs the structure of undirected consensus networks with correlated noise. We illustrate the effectiveness of the method developed through numerical simulations as well as experiments on a five node network of Raspberry Pis.

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