Topology inference of directed graphs using nonlinear structural vector autoregressive models

Yanning Shen, Brian Baingana, Georgios B. Giannakis

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

7 Scopus citations

Abstract

Linear structural vector autoregressive models constitute a generalization of structural equation models (SEMs) and vector autoregressive (VAR) models, two popular approaches for topology inference of directed graphs. Although simple and tractable, linear SVARMs seldom capture nonlinearities that are inherent to complex systems, such as the human brain. To this end, the present paper advocates kernel-based nonlinear SVARMs, and develops an efficient sparsity-promoting least-squares estimator to learn the hidden topology. Numerical tests on real electrocorticographic (ECoG) data from an Epilepsy study corroborate the efficacy of the novel approach.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6513-6517
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Publication series

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

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Keywords

  • Network topology inference
  • nonlinear
  • structural vector autoregressive models

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    Shen, Y., Baingana, B., & Giannakis, G. B. (2017). Topology inference of directed graphs using nonlinear structural vector autoregressive models. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 6513-6517). [7953411] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7953411