Online Graph Learning from Time-Varying Structural Equation Models
conference paper
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 stream ing 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.
Topics
Dynamic topology identificationgraph learninggraph signal processingtime-varying optimizationE-learningEconomicsLearning systemsSignal processingStatisticsTime varying control systemsTime varying networksDynamic topologiesDynamic topology identificationGraph learningGraph signal processingOptimisationsSignal-processingStructural equation modelsTime varyingTime-varying optimizationTopology identificationTopology
TNO Identifier
967245
ISSN
10586393
ISBN
9781665458283
Publisher
IEEE Computer Society
Source title
Conference Record - Asilomar Conference on Signals, Systems and Computers, 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021, 31 October 2021 through 3 November 2021
Editor(s)
Matthews, M.B.
Pages
1579-1585
Files
To receive the publication files, please send an e-mail request to TNO Repository.