Online Time-Varying Topology Identification Via Prediction-Correction Algorithms

Alberto Natali, Mario Coutino, Elvin Isufi, Geert Leus

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

1 Scopus citations

Abstract

Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has to be learned from data. Topology identification is a challenging task, as the problem is often ill-posed, and becomes even harder when the graph structure is time-varying. In this paper, we address the problem of dynamic topology identification by building on recent results from time-varying optimization, devising a general-purpose online algorithm operating in non-stationary environments. Because of its iteration-constrained nature, the proposed approach exhibits an intrinsic temporal-regularization of the graph topology without explicitly enforcing it. As a case-study, we specialize our method to the Gaussian graphical model (GGM) problem and corroborate its performance.
Original languageEnglish (US)
Title of host publicationICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages5400-5404
Number of pages5
ISBN (Print)9781728176055
DOIs
StatePublished - Jun 6 2021
Externally publishedYes

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