Topology inference of multilayer networks

Panagiotis A. Traganitis; Yanning Shen; Georgios B Giannakis

doi:10.1109/INFCOMW.2017.8116495

Topology inference of multilayer networks

Panagiotis A. Traganitis, Yanning Shen, Georgios B Giannakis

Electrical and Computer Engineering

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

6 Scopus citations

Abstract

Linear structural equation models (SEMs) have been very successful in identifying the topology of complex graphs, such as those representing tactical, social and brain networks. The rising popularity of multilayer networks, presents the need for tools that are tailored to leverage the layered structure of the underlying network. To this end, a multilayer SEM is put forth, to infer causal relations between nodes belonging to multilayer networks. An efficient algorithm based on the alternating direction method of multipliers (ADMM) is developed, and preliminary tests on synthetic as well as real data demonstrate the effectiveness of the proposed approach.

Original language	English (US)
Title of host publication	2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	898-903
Number of pages	6
ISBN (Electronic)	9781538627846
DOIs	https://doi.org/10.1109/INFCOMW.2017.8116495
State	Published - Nov 20 2017
Event	2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017 - Atlanta, United States Duration: May 1 2017 → May 4 2017

Publication series

Name	2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017

Other

Other	2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017
Country/Territory	United States
City	Atlanta
Period	5/1/17 → 5/4/17

Keywords

Multilayer networks
Structural Equation Models
Topology inference

Publisher link

10.1109/INFCOMW.2017.8116495

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Traganitis, P. A., Shen, Y., & Giannakis, G. B. (2017). Topology inference of multilayer networks. In 2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017 (pp. 898-903). [8116495] (2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFCOMW.2017.8116495

Topology inference of multilayer networks. / Traganitis, Panagiotis A.; Shen, Yanning; Giannakis, Georgios B.

2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 898-903 8116495 (2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017).

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

Traganitis, PA, Shen, Y & Giannakis, GB 2017, Topology inference of multilayer networks. in 2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017., 8116495, 2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017, Institute of Electrical and Electronics Engineers Inc., pp. 898-903, 2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017, Atlanta, United States, 5/1/17. https://doi.org/10.1109/INFCOMW.2017.8116495

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title = "Topology inference of multilayer networks",

abstract = "Linear structural equation models (SEMs) have been very successful in identifying the topology of complex graphs, such as those representing tactical, social and brain networks. The rising popularity of multilayer networks, presents the need for tools that are tailored to leverage the layered structure of the underlying network. To this end, a multilayer SEM is put forth, to infer causal relations between nodes belonging to multilayer networks. An efficient algorithm based on the alternating direction method of multipliers (ADMM) is developed, and preliminary tests on synthetic as well as real data demonstrate the effectiveness of the proposed approach.",

keywords = "Multilayer networks, Structural Equation Models, Topology inference",

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

year = "2017",

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doi = "10.1109/INFCOMW.2017.8116495",

language = "English (US)",

series = "2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017",

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

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booktitle = "2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017",

note = "2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017 ; Conference date: 01-05-2017 Through 04-05-2017",

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T1 - Topology inference of multilayer networks

AU - Traganitis, Panagiotis A.

AU - Shen, Yanning

AU - Giannakis, Georgios B

PY - 2017/11/20

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N2 - Linear structural equation models (SEMs) have been very successful in identifying the topology of complex graphs, such as those representing tactical, social and brain networks. The rising popularity of multilayer networks, presents the need for tools that are tailored to leverage the layered structure of the underlying network. To this end, a multilayer SEM is put forth, to infer causal relations between nodes belonging to multilayer networks. An efficient algorithm based on the alternating direction method of multipliers (ADMM) is developed, and preliminary tests on synthetic as well as real data demonstrate the effectiveness of the proposed approach.

AB - Linear structural equation models (SEMs) have been very successful in identifying the topology of complex graphs, such as those representing tactical, social and brain networks. The rising popularity of multilayer networks, presents the need for tools that are tailored to leverage the layered structure of the underlying network. To this end, a multilayer SEM is put forth, to infer causal relations between nodes belonging to multilayer networks. An efficient algorithm based on the alternating direction method of multipliers (ADMM) is developed, and preliminary tests on synthetic as well as real data demonstrate the effectiveness of the proposed approach.

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KW - Structural Equation Models

KW - Topology inference

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SP - 898

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ER -

Topology inference of multilayer networks

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