State-space subspace techniques recover network topology from partial input-output data of continuous-time linear systems, with a convergent alternating-projections algorithm.
The emerging field of signal processing on gr aphs: Extending high-dimensional data analysis to networks and other irregular domains
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State-Space Network Topology Identification from Partial Observations
State-space subspace techniques recover network topology from partial input-output data of continuous-time linear systems, with a convergent alternating-projections algorithm.