Topology Learning of Radial Dynamical Systems with Latent Nodes

Saurav Talukdar, Deepjyoti Deka, Michael Chertkov, Murti Salapaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


In this article, we present a method to reconstruct the topology of a partially observed radial network of linear dynamical systems with bi-directional interactions. Our approach exploits the structure of the inverse power spectral density matrix and recovers edges involving nodes up to four hops away in the underlying topology. We then present an algorithm with provable guarantees, which eliminates the spurious links obtained and also identifies the location of the unobserved nodes in the inferred topology. The algorithm recovers the exact topology of the network by using only time-series of the states at the observed nodes. The effectiveness of the method developed is demonstrated by applying it on a typical distribution system of the electric grid.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781538654286
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2018 AACC.


Dive into the research topics of 'Topology Learning of Radial Dynamical Systems with Latent Nodes'. Together they form a unique fingerprint.

Cite this