Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Coherence resonance in influencer networks

Full text
Author(s):
Toenjes, Ralf [1] ; Fiore, Carlos E. [2] ; Pereira, Tiago [3, 4]
Total Authors: 3
Affiliation:
[1] Univ Potsdam, Inst Phys & Astron, Karl Liebknecht Str 24, D-14476 Potsdam - Germany
[2] Univ Sao Paulo, Inst Fis, Sao Paulo - Brazil
[3] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Sao Carlos, SP - Brazil
[4] Imperial Coll London, Dept Math, London SW7 2AZ - England
Total Affiliations: 4
Document type: Journal article
Source: NATURE COMMUNICATIONS; v. 12, n. 1 JAN 4 2021.
Web of Science Citations: 1
Abstract

Complex networks are abundant in nature and many share an important structural property: they contain a few nodes that are abnormally highly connected (hubs). Some of these hubs are called influencers because they couple strongly to the network and play fundamental dynamical and structural roles. Strikingly, despite the abundance of networks with influencers, little is known about their response to stochastic forcing. Here, for oscillatory dynamics on influencer networks, we show that subjecting influencers to an optimal intensity of noise can result in enhanced network synchronization. This new network dynamical effect, which we call coherence resonance in influencer networks, emerges from a synergy between network structure and stochasticity and is highly nonlinear, vanishing when the noise is too weak or too strong. Our results reveal that the influencer backbone can sharply increase the dynamical response in complex systems of coupled oscillators. Influencer networks include a small set of highly-connected nodes and can reach synchrony only via strong node interaction. Tonjes et al. show that introducing an optimal amount of noise enhances synchronization of such networks, which may be relevant for neuroscience or opinion dynamics applications. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 15/04451-2 - Phase transitions: methods and absorbing states systems
Grantee:Carlos Eduardo Fiore dos Santos
Support Opportunities: Regular Research Grants
FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support Opportunities: Research Projects - Thematic Grants