ONLINE IDENTIFICATION of DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC and NONLINEAR DEPENDENCIES

Yanning Shen, Georgios B. Giannakis

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

4 Scopus citations

Abstract

Linear structural vector autoregressive models (SVARMs) have well-documented merits for topology inference of directional graphs emerging in diverse applications, including gene-regulatory, brain, and social networks. Although simple and tractable, linear SVARMs cannot capture nonlinearities that are inherent to complex systems, such as the human brain, that can also vary over time. Given nodal measurements, these considerations motivate the dynamic nonlinear SVARM approach developed here to track the possibly directed and dynamic nonlinear interactions among network nodes. For slow-varying topologies, nonlinear model parameters are estimated via functional stochastic gradient descent. Numerical tests showcase the effectiveness of the novel algorithms in unveiling sparse dynamically-evolving topologies.

Original languageEnglish (US)
Title of host publication2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages195-199
Number of pages5
ISBN (Print)9781538644102
DOIs
StatePublished - Aug 17 2018
Event2018 IEEE Data Science Workshop, DSW 2018 - Lausanne, Switzerland
Duration: Jun 4 2018Jun 6 2018

Publication series

Name2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

Other

Other2018 IEEE Data Science Workshop, DSW 2018
CountrySwitzerland
CityLausanne
Period6/4/186/6/18

Bibliographical note

Funding Information:
† Work was supported by NSF grants 1500713, 1514056, and 1711471.

Keywords

  • Network topology inference
  • dynamics
  • nonlinear
  • structural vector autoregressive models

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