Online Graph Learning From Time-Varying Structural Equation Models

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Topology identification is an important problem across many disciplines, since it reveals pairwise interactions among entities and can be used to interpret graph data. In many scenarios, however, this (unknown) topology is time-varying, rendering the problem even harder. In this paper, we focus on a time-varying version of the structural equation modeling (SEM) framework, which is an umbrella of multivariate techniques widely adopted in econometrics, epidemiology and psychology. In particular, we view the linear SEM as a first-order diffusion of a signal over a graph whose topology changes over time. Our goal is to learn such time-varying topology from streaming data. To attain this goal, we propose a real-time algorithm, further accelerated by building on recent advances in time-varying optimization, which updates the time-varying solution as a new sample comes into the system. We augment the implementation steps with theoretical guarantees, and we show performances on synthetic and real datasets.
Original languageEnglish
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Subtitle of host publicationProceedings
EditorsMichael B. Matthews
Number of pages7
ISBN (Electronic)978-1-6654-5828-3
ISBN (Print)978-1-6654-5829-0
Publication statusPublished - 2021
Event2021 55th Asilomar Conference on Signals, Systems, and Computers - Pacific Grove, United States
Duration: 31 Oct 20213 Nov 2021
Conference number: 55th

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Conference2021 55th Asilomar Conference on Signals, Systems, and Computers
Country/TerritoryUnited States
CityPacific Grove

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Dynamic topology identification
  • graph signal processing
  • graph learning
  • time-varying optimization


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