SEMI-BLIND INFERENCE of TOPOLOGIES and SIGNALS over GRAPHS

Vassilis N. Ioannidis; Yanning Shen; Georgios B Giannakis

doi:10.1109/DSW.2018.8439117

SEMI-BLIND INFERENCE of TOPOLOGIES and SIGNALS over GRAPHS

Vassilis N. Ioannidis, Yanning Shen, Georgios B Giannakis

Electrical and Computer Engineering

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

4 Scopus citations

Abstract

Network science provides valuable insights across numerous disciplines including sociology, biology, neuroscience and engineering. A task of major practical importance in these application domains is inferring the network topology from noisy observations over a limited subset of nodes. This work presents a novel approach for joint inference of the network topology and estimation of graph signals from partial nodal observations based on structural equation models (SEMs). SEMs have well-documented merits in identifying the directed topology of complex graphs by capturing causal relationships among nodes. The resultant algorithm iterates between inferring a directed graph that 'best' fits the data, and estimating the graph signals over the learned graph. Numerical tests with synthetic as well as real data corroborate the effectiveness of the joint inference approach.

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	165-169
Number of pages	5
ISBN (Print)	9781538644102
DOIs	https://doi.org/10.1109/DSW.2018.8439117
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:
The work in this paper was supported by NSF grant 1500713, and NIH 1R01GM104975-01.

Publisher Copyright:
© 2018 IEEE.

Keywords

Graph signal reconstruction
directed graphs
structural equation models
topology inference

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10.1109/DSW.2018.8439117

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SEMI-BLIND INFERENCE of TOPOLOGIES and SIGNALS over GRAPHS. / Ioannidis, Vassilis N.; Shen, Yanning; Giannakis, Georgios B.
2018 IEEE Data Science Workshop, DSW 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 165-169 8439117 (2018 IEEE Data Science Workshop, DSW 2018 - Proceedings).

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

Ioannidis, VN, Shen, Y & Giannakis, GB 2018, SEMI-BLIND INFERENCE of TOPOLOGIES and SIGNALS over GRAPHS. in 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings., 8439117, 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 165-169, 2018 IEEE Data Science Workshop, DSW 2018, Lausanne, Switzerland, 6/4/18. https://doi.org/10.1109/DSW.2018.8439117

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abstract = "Network science provides valuable insights across numerous disciplines including sociology, biology, neuroscience and engineering. A task of major practical importance in these application domains is inferring the network topology from noisy observations over a limited subset of nodes. This work presents a novel approach for joint inference of the network topology and estimation of graph signals from partial nodal observations based on structural equation models (SEMs). SEMs have well-documented merits in identifying the directed topology of complex graphs by capturing causal relationships among nodes. The resultant algorithm iterates between inferring a directed graph that 'best' fits the data, and estimating the graph signals over the learned graph. Numerical tests with synthetic as well as real data corroborate the effectiveness of the joint inference approach.",

keywords = "Graph signal reconstruction, directed graphs, structural equation models, topology inference",

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

note = "Funding Information: The work in this paper was supported by NSF grant 1500713, and NIH 1R01GM104975-01. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Data Science Workshop, DSW 2018 ; Conference date: 04-06-2018 Through 06-06-2018",

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SEMI-BLIND INFERENCE of TOPOLOGIES and SIGNALS over GRAPHS

Abstract

Publication series

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Bibliographical note

Keywords

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