State-Space Network Topology Identification From Partial Observations

In this article, we explore the state-space formulation of a network process to recover from partial observations the network topology that drives its dynamics. To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology identificati...

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Bibliographic Details
Published in:IEEE transactions on signal and information processing over networks Vol. 6; pp. 211 - 225
Main Authors: Coutino, Mario, Isufi, Elvin, Maehara, Takanori, Leus, Geert
Format: Journal Article
Language:English
Published: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Continuous time systems
Evolution (biology)
graph signal processing
Information processing
Inverse eigenvalue problems
Mathematical analysis
Measurement
Modeling
Network control
Network topologies
Network topology
network topology identification
Signal processing
signal processing over networks
state-space models
System dynamics
System identification
Topology
ISSN:2373-776X, 2373-7778
Online Access:Get full text
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Summary:In this article, we explore the state-space formulation of a network process to recover from partial observations the network topology that drives its dynamics. To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology identification problem. This approach provides a unified view of network control and signal processing on graphs. In addition, we provide theoretical guarantees for the recovery of the topological structure of a deterministic continuous-time linear dynamical system from input-output observations even when the input and state interaction networks are different. Our mathematical analysis is accompanied by an algorithm for identifying from data,a network topology consistent with the system dynamics and conforms to the prior information about the underlying structure. The proposed algorithm relies on alternating projections and is provably convergent. Numerical results corroborate the theoretical findings and the applicability of the proposed algorithm.
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ISSN:2373-776X
2373-7778
DOI:10.1109/TSIPN.2020.2975393