Name
Abstract | ||
|---|---|---|
We consider a network of interconnected dynamical systems. Spectral network identification consists in recovering the eigenvalues of the network Laplacian from the measurements of a very limited number (possibly one) of signals. These eigenvalues allow to deduce some global properties of the network, such as bounds on the node degree. Having recently introduced this approach for autonomous networks of nonlinear systems, we extend it here to treat networked systems with external inputs on the nodes, in the case of linear dynamics. This is more natural in several applications, and removes the need to sometimes use several independent trajectories. We illustrate our framework with several examples, where we estimate the mean, minimum, and maximum node degree in the network. Inferring some information on the leading Laplacian eigenvectors, we also use our framework in the context of network clustering. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CDC.2017.8263708 | 2017 IEEE 56th Annual Conference on Decision and Control (CDC) |
Keywords | DocType | Volume |
maximum node degree,network clustering,interconnected dynamical systems,spectral network identification,eigenvalues,network Laplacian,autonomous networks,nonlinear systems,networked systems,external inputs,linear dynamics,mean node degree,minimum node degree,Laplacian eigenvectors | Conference | abs/1709.04153 |
ISSN | ISBN | Citations |
0743-1546 | 978-1-5090-2874-0 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandre Mauroy | 1 | 59 | 8.21 |
| Julien M. Hendrickx | 2 | 772 | 77.11 |