NETWORK INFERENCE from COMPLEX SYSTEMS STEADY STATES OBSERVATIONS: THEORY and METHODS

Hoi To Wai, Anna Scaglione, Baruch Barzel, Amir Leshem

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents new results on network inference from observations of steady state behaviors emerging from perturbations of complex networks dynamics. We focus on the estimation of network and flow parameters using a general regularized inference formulation, which is tackled numerically using the standard technique of alternating optimization. We argue that relying only on the steady states equations removes the requirement of precisely recording transient data, and allows to meaningfully combine data from multiple experiments. To provide theoretical benchmarks we study the relationship between topological and functional characteristics of the system and the divergence between the steady state behavior observed, to give rigorous performance benchmarks. Numerical results are presented on examples with social networks and gene regulatory networks to justify our claims.

Original languageAmerican English
Title of host publication2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages155-159
Number of pages5
ISBN (Print)9781538644102
DOIs
StatePublished - 17 Aug 2018
Externally publishedYes
Event2018 IEEE Data Science Workshop, DSW 2018 - Lausanne, Switzerland
Duration: 4 Jun 20186 Jun 2018

Publication series

Name2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

Conference

Conference2018 IEEE Data Science Workshop, DSW 2018
Country/TerritorySwitzerland
CityLausanne
Period4/06/186/06/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • complex network systems
  • gene networks
  • network identifiability
  • network inference
  • social networks

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