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 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 languageEnglish (US)
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 - Aug 17 2018
Event2018 IEEE Data Science Workshop, DSW 2018 - Lausanne, Switzerland
Duration: Jun 4 2018Jun 6 2018

Other

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

Fingerprint

Complex networks
Large scale systems
Complex Systems
Genes
Benchmark
Network Dynamics
Experiments
Gene Regulatory Network
social network
State Equation
Complex Dynamics
Complex Networks
Justify
Social Networks
Divergence
divergence
perturbation
Perturbation
Numerical Results
Optimization

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

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). [8439896] 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; Leshem, Amir.

2018 IEEE Data Science Workshop, DSW 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 155-159 8439896.

Research output: Chapter in Book/Report/Conference proceedingConference 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, 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
Wai HT, Scaglione A, Barzel B, Leshem A. NETWORK INFERENCE from COMPLEX SYSTEMS STEADY STATES OBSERVATIONS: THEORY and METHODS. In 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 155-159. 8439896 https://doi.org/10.1109/DSW.2018.8439896
Wai, Hoi To ; Scaglione, Anna ; Barzel, Baruch ; Leshem, Amir. / NETWORK INFERENCE from COMPLEX SYSTEMS STEADY STATES OBSERVATIONS : THEORY and METHODS. 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 155-159
@inproceedings{8a54926664d549f4841cbcefa7abbf31,
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",
year = "2018",
month = "8",
day = "17",
doi = "10.1109/DSW.2018.8439896",
language = "English (US)",
isbn = "9781538644102",
pages = "155--159",
booktitle = "2018 IEEE Data Science Workshop, DSW 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - NETWORK INFERENCE from COMPLEX SYSTEMS STEADY STATES OBSERVATIONS

T2 - THEORY and METHODS

AU - Wai, Hoi To

AU - Scaglione, Anna

AU - Barzel, Baruch

AU - Leshem, Amir

PY - 2018/8/17

Y1 - 2018/8/17

N2 - 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.

AB - 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.

KW - complex network systems

KW - gene networks

KW - network identifiability

KW - network inference

KW - social networks

UR - http://www.scopus.com/inward/record.url?scp=85053107321&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053107321&partnerID=8YFLogxK

U2 - 10.1109/DSW.2018.8439896

DO - 10.1109/DSW.2018.8439896

M3 - Conference contribution

AN - SCOPUS:85053107321

SN - 9781538644102

SP - 155

EP - 159

BT - 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -