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

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

doi:10.1109/DSW.2018.8439896

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 proceeding › Conference contribution

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 language	English (US)
Title of host publication	2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	155-159
Number of pages	5
ISBN (Print)	9781538644102
DOIs	https://doi.org/10.1109/DSW.2018.8439896
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

Keywords

complex network systems
gene networks
network identifiability
network inference
social networks

ASJC Scopus subject areas

Artificial Intelligence
Safety, Risk, Reliability and Quality
Water Science and Technology
Control and Optimization

Access to Document

10.1109/DSW.2018.8439896

Cite this

Wai, H. T., Scaglione, A., Barzel, B., & Leshem, A. (2018). NETWORK INFERENCE from COMPLEX SYSTEMS STEADY STATES OBSERVATIONS: THEORY and METHODS. In 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings (pp. 155-159). Article 8439896 (2018 IEEE Data Science Workshop, DSW 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSW.2018.8439896

NETWORK INFERENCE from COMPLEX SYSTEMS STEADY STATES OBSERVATIONS: THEORY and METHODS. / Wai, Hoi To; Scaglione, Anna; Barzel, Baruch et al.
2018 IEEE Data Science Workshop, DSW 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 155-159 8439896 (2018 IEEE Data Science Workshop, DSW 2018 - Proceedings).

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

Wai, HT, Scaglione, A, Barzel, B & Leshem, A 2018, NETWORK INFERENCE from COMPLEX SYSTEMS STEADY STATES OBSERVATIONS: THEORY and METHODS. in 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings., 8439896, 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 155-159, 2018 IEEE Data Science Workshop, DSW 2018, Lausanne, Switzerland, 6/4/18. https://doi.org/10.1109/DSW.2018.8439896

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title = "NETWORK INFERENCE from COMPLEX SYSTEMS STEADY STATES OBSERVATIONS: THEORY and METHODS",

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.",

keywords = "complex network systems, gene networks, network identifiability, network inference, social networks",

author = "Wai, {Hoi To} and Anna Scaglione and Baruch Barzel and Amir Leshem",

note = "Funding Information: This work is supported by NSF CCF-BSF 1714672. 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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NETWORK INFERENCE from COMPLEX SYSTEMS STEADY STATES OBSERVATIONS: THEORY and METHODS

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Keywords

ASJC Scopus subject areas

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