ONLINE IDENTIFICATION of DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC and NONLINEAR DEPENDENCIES

Yanning Shen; Georgios B. Giannakis

doi:10.1109/DSW.2018.8439119

ONLINE IDENTIFICATION of DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC and NONLINEAR DEPENDENCIES

Yanning Shen, Georgios B. Giannakis

Electrical and Computer Engineering

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

13 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 language	English (US)
Title of host publication	2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	195-199
Number of pages	5
ISBN (Print)	9781538644102
DOIs	https://doi.org/10.1109/DSW.2018.8439119
State	Published - Aug 17 2018
Event	2018 IEEE Data Science Workshop, DSW 2018 - Lausanne, Switzerland Duration: Jun 4 2018 → Jun 6 2018

Publication series

Name	2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

Other

Other	2018 IEEE Data Science Workshop, DSW 2018
Country/Territory	Switzerland
City	Lausanne
Period	6/4/18 → 6/6/18

Bibliographical note

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

Publisher Copyright:
© 2018 IEEE.

Keywords

Network topology inference
dynamics
nonlinear
structural vector autoregressive models

Access

10.1109/DSW.2018.8439119

OpenUrl availability

Full text

Cite this

Shen, Y., & Giannakis, G. B. (2018). ONLINE IDENTIFICATION of DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC and NONLINEAR DEPENDENCIES. In 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings (pp. 195-199). Article 8439119 (2018 IEEE Data Science Workshop, DSW 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSW.2018.8439119

ONLINE IDENTIFICATION of DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC and NONLINEAR DEPENDENCIES. / Shen, Yanning; Giannakis, Georgios B.
2018 IEEE Data Science Workshop, DSW 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 195-199 8439119 (2018 IEEE Data Science Workshop, DSW 2018 - Proceedings).

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

Shen, Y & Giannakis, GB 2018, ONLINE IDENTIFICATION of DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC and NONLINEAR DEPENDENCIES. in 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings., 8439119, 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 195-199, 2018 IEEE Data Science Workshop, DSW 2018, Lausanne, Switzerland, 6/4/18. https://doi.org/10.1109/DSW.2018.8439119

@inproceedings{0a2f1d1178164c77b04cc471ffcbac17,

title = "ONLINE IDENTIFICATION of DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC and NONLINEAR DEPENDENCIES",

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.",

keywords = "Network topology inference, dynamics, nonlinear, structural vector autoregressive models",

author = "Yanning Shen and Giannakis, {Georgios B.}",

note = "Funding Information: † Work was supported by NSF grants 1500713, 1514056, and 1711471. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Data Science Workshop, DSW 2018 ; Conference date: 04-06-2018 Through 06-06-2018",

year = "2018",

month = aug,

day = "17",

doi = "10.1109/DSW.2018.8439119",

language = "English (US)",

isbn = "9781538644102",

series = "2018 IEEE Data Science Workshop, DSW 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "195--199",

booktitle = "2018 IEEE Data Science Workshop, DSW 2018 - Proceedings",

}

TY - GEN

T1 - ONLINE IDENTIFICATION of DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC and NONLINEAR DEPENDENCIES

AU - Shen, Yanning

AU - Giannakis, Georgios B.

PY - 2018/8/17

Y1 - 2018/8/17

N2 - 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.

AB - 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.

KW - Network topology inference

KW - dynamics

KW - nonlinear

KW - structural vector autoregressive models

UR - http://www.scopus.com/inward/record.url?scp=85053133920&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053133920&partnerID=8YFLogxK

U2 - 10.1109/DSW.2018.8439119

DO - 10.1109/DSW.2018.8439119

M3 - Conference contribution

AN - SCOPUS:85053133920

SN - 9781538644102

T3 - 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

SP - 195

EP - 199

BT - 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Data Science Workshop, DSW 2018

Y2 - 4 June 2018 through 6 June 2018

ER -

ONLINE IDENTIFICATION of DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC and NONLINEAR DEPENDENCIES

Abstract

Publication series

Other

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this