Network topology inference via elastic net structural equation models

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

5 Scopus citations

Abstract

Linear structural equation models (SEMs) have been very successful in identifying the topology of complex graphs, such as those representing social and brain networks. In many cases however, the presence of highly correlated nodes hinders performance of the available SEM estimators that rely on the least-Absolute shrinkage and selection operator (LASSO). To this end, an elastic net based SEM is put forth, to infer causal relations between nodes belonging to networks, in the presence of highly correlated data. 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 languageEnglish (US)
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages146-150
Number of pages5
ISBN (Electronic)9780992862671
DOIs
StatePublished - Oct 23 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: Aug 28 2017Sep 2 2017

Publication series

Name25th European Signal Processing Conference, EUSIPCO 2017
Volume2017-January

Other

Other25th European Signal Processing Conference, EUSIPCO 2017
CountryGreece
CityKos
Period8/28/179/2/17

Bibliographical note

Publisher Copyright:
© EURASIP 2017.

Keywords

  • Elastic Net
  • Networks
  • Structural Equation Models
  • Topology inference

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