Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization

Yanning Shen; Brian Baingana; Georgios B. Giannakis

doi:10.1109/GlobalSIP.2016.7905867

Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization

Yanning Shen
, Brian Baingana
, Georgios B. Giannakis

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	375-379
Number of pages	5
ISBN (Electronic)	9781509045457
DOIs	https://doi.org/10.1109/GlobalSIP.2016.7905867
State	Published - Apr 19 2017
Event	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States Duration: Dec 7 2016 → Dec 9 2016

Publication series

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

Other

Other	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Country/Territory	United States
City	Washington
Period	12/7/16 → 12/9/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

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

Publisher link

10.1109/GlobalSIP.2016.7905867

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Shen, Y., Baingana, B., & Giannakis, G. B. (2017). Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings (pp. 375-379). Article 7905867 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2016.7905867

Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization. / Shen, Yanning; Baingana, Brian; Giannakis, Georgios B.
2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 375-379 7905867 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shen, Y, Baingana, B & Giannakis, GB 2017, Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization. in 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings., 7905867, 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 375-379, 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016, Washington, United States, 12/7/16. https://doi.org/10.1109/GlobalSIP.2016.7905867

Shen Y, Baingana B, Giannakis GB. Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 375-379. 7905867. (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). doi: 10.1109/GlobalSIP.2016.7905867

Shen, Yanning ; Baingana, Brian ; Giannakis, Georgios B. / Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization. 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 375-379 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings).

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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.",

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Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization

Abstract

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Keywords

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