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INFERENCE OF TIME-VARYING GRAPH TOPOLOGIES VIA GAUSSIAN PROCESSES

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, we explore the estimation of directed time-varying graph topologies, which represent evolving relationships among nodes in high-dimensional, interdependent data. We introduce a novel fully Bayesian method based on Gaussian processes, employing random walks to model the time-varying edge weights with time. This approach accommodates nonlinear and time-varying lagged relationships among time series. We implement the proposed method using the Hamiltonian Monte Carlo method. Numerical tests reveal that our method performs very well and is comparable to state-of-the-art methods, making it a promising tool for unveiling dynamic graph structures and causality.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages13276-13280
Number of pages5
ISBN (Electronic)9798350344851
DOIs
StatePublished - 2024
Event2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Seoul, Korea, Republic of
Duration: Apr 14 2024Apr 19 2024

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
Country/TerritoryKorea, Republic of
CitySeoul
Period04/14/2404/19/24

Keywords

  • Gaussian processes
  • Hamiltonian Monte Carlo
  • automatic relevance determination
  • time-varying graphs

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