SEMI-BLIND INFERENCE of TOPOLOGIES and SIGNALS over GRAPHS

Vassilis N. Ioannidis, Yanning Shen, Georgios B Giannakis

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

3 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 languageEnglish (US)
Title of host publication2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages165-169
Number of pages5
ISBN (Print)9781538644102
DOIs
StatePublished - Aug 17 2018
Event2018 IEEE Data Science Workshop, DSW 2018 - Lausanne, Switzerland
Duration: Jun 4 2018Jun 6 2018

Publication series

Name2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

Other

Other2018 IEEE Data Science Workshop, DSW 2018
Country/TerritorySwitzerland
CityLausanne
Period6/4/186/6/18

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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