Self-Organization of Weighted Networks for Optimal Synchronizability

Louis Kempton; Guido Herrmann; Mario Di Bernardo

doi:10.1109/TCNS.2014.2301653

Self-Organization of Weighted Networks for Optimal Synchronizability

Louis Kempton^*
, Guido Herrmann
, Mario Di Bernardo

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

23 Citations (Scopus)

513 Downloads (Pure)

Abstract

We show that a network can self-organize its existing topology, i.e., by adapting edge weights, in a completely decentralized manner in order to maximize its synchronizability while satisfying local constraints: we look specifically at nonnegativity of edge weights and maximum weighted degree of nodes. A novel multilayer approach is presented, which uses a decentralized strategy through which each node can estimate one of two spectral functions of the graph Laplacian, the algebraic connectivity λ₂, or the eigenratio r=λ_n / λ₂. These local estimates are then used to evolve the edge weights so as to maximize λ₂, or minimize r, and, hence, achieve globally optimal values for the edge weights for the synchronization of a network of coupled systems.

Original language	English
Article number	7993095
Pages (from-to)	1541-1550
Number of pages	10
Journal	IEEE Transactions on Control of Network Systems
Volume	5
Issue number	4
Early online date	26 Jul 2017
DOIs	https://doi.org/10.1109/TCNS.2014.2301653 https://doi.org/10.1109/TCNS.2017.2732161
Publication status	Published - 1 Dec 2018

Research Groups and Themes

Engineering Mathematics Research Group
Bristol BioDesign Institute

Keywords

synthetic biology
decentralized control
optimization
Complex networks

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title = "Self-Organization of Weighted Networks for Optimal Synchronizability",

abstract = "We show that a network can self-organize its existing topology, i.e., by adapting edge weights, in a completely decentralized manner in order to maximize its synchronizability while satisfying local constraints: we look specifically at nonnegativity of edge weights and maximum weighted degree of nodes. A novel multilayer approach is presented, which uses a decentralized strategy through which each node can estimate one of two spectral functions of the graph Laplacian, the algebraic connectivity λ2, or the eigenratio r=λn / λ2. These local estimates are then used to evolve the edge weights so as to maximize λ2, or minimize r, and, hence, achieve globally optimal values for the edge weights for the synchronization of a network of coupled systems.",

keywords = "synthetic biology, decentralized control, optimization, Complex networks",

author = "Louis Kempton and Guido Herrmann and \{Di Bernardo\}, Mario",

year = "2018",

month = dec,

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doi = "10.1109/TCNS.2014.2301653",

language = "English",

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T1 - Self-Organization of Weighted Networks for Optimal Synchronizability

AU - Kempton, Louis

AU - Herrmann, Guido

AU - Di Bernardo, Mario

PY - 2018/12/1

Y1 - 2018/12/1

N2 - We show that a network can self-organize its existing topology, i.e., by adapting edge weights, in a completely decentralized manner in order to maximize its synchronizability while satisfying local constraints: we look specifically at nonnegativity of edge weights and maximum weighted degree of nodes. A novel multilayer approach is presented, which uses a decentralized strategy through which each node can estimate one of two spectral functions of the graph Laplacian, the algebraic connectivity λ2, or the eigenratio r=λn / λ2. These local estimates are then used to evolve the edge weights so as to maximize λ2, or minimize r, and, hence, achieve globally optimal values for the edge weights for the synchronization of a network of coupled systems.

AB - We show that a network can self-organize its existing topology, i.e., by adapting edge weights, in a completely decentralized manner in order to maximize its synchronizability while satisfying local constraints: we look specifically at nonnegativity of edge weights and maximum weighted degree of nodes. A novel multilayer approach is presented, which uses a decentralized strategy through which each node can estimate one of two spectral functions of the graph Laplacian, the algebraic connectivity λ2, or the eigenratio r=λn / λ2. These local estimates are then used to evolve the edge weights so as to maximize λ2, or minimize r, and, hence, achieve globally optimal values for the edge weights for the synchronization of a network of coupled systems.

KW - synthetic biology

KW - decentralized control

KW - optimization

KW - Complex networks

UR - https://www.scopus.com/pages/publications/85028946413

U2 - 10.1109/TCNS.2014.2301653

DO - 10.1109/TCNS.2014.2301653

M3 - Article (Academic Journal)

AN - SCOPUS:85028946413

SN - 2325-5870

VL - 5

SP - 1541

EP - 1550

JO - IEEE Transactions on Control of Network Systems

JF - IEEE Transactions on Control of Network Systems

IS - 4

M1 - 7993095

ER -

Self-Organization of Weighted Networks for Optimal Synchronizability

Abstract

Research Groups and Themes

Keywords

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Distributed Control and Optimisation of Complex Networks via their Laplacian Spectra

Cite this

Self-Organization of Weighted Networks for Optimal Synchronizability

Abstract

Research Groups and Themes

Keywords

Access to Document

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Other files and links

Fingerprint

Student theses

Distributed Control and Optimisation of Complex Networks via their Laplacian Spectra

Cite this