Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization

Yanning Shen, Brian Baingana, Georgios B. Giannakis

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

1 Scopus citations

Abstract

This paper deals with tracking dynamic piecewise-constant network topologies that underpin complex systems including online social networks, neural pathways in the brain, and the world-wide web. Leveraging a structural equation model (SEM) in which only second-order statistics of exogenous inputs are known, the topology inference problem is recast using three-way tensors constructed from observed nodal data. To facilitate real-time operation, an adaptive parallel factor (PARAFAC) tensor decomposition is advocated to track the topology-revealing tensor factors. Preliminary tests on simulated data corroborate the effectiveness of the novel tensor-based approach.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages375-379
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
CountryUnited States
CityWashington
Period12/7/1612/9/16

Bibliographical note

Funding Information:
Work in this paper was supported by grants NSF 1500713, 1514056 and NIH 1R01GM104975-01.

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Dynamic networks
  • Network inference
  • Structural equation models (SEMs)
  • Tensor decomposition

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