Individual nodes contribution to the mesoscale of complex networks

Florian Klimm, Javier Borge-Holthoefer, Niels Wessel, Jürgen Kurths, Gorka Zamora-Lopez

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.

Original languageEnglish
Article number125006
JournalNew Journal of Physics
Volume16
DOIs
Publication statusPublished - 2 Dec 2014

Fingerprint

modules
tuberculosis
hubs
topology
configurations

Keywords

  • community structure
  • genetic regulatory networks
  • network metrics
  • neuronal networks

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Klimm, F., Borge-Holthoefer, J., Wessel, N., Kurths, J., & Zamora-Lopez, G. (2014). Individual nodes contribution to the mesoscale of complex networks. New Journal of Physics, 16, [125006]. https://doi.org/10.1088/1367-2630/16/12/125006

Individual nodes contribution to the mesoscale of complex networks. / Klimm, Florian; Borge-Holthoefer, Javier; Wessel, Niels; Kurths, Jürgen; Zamora-Lopez, Gorka.

In: New Journal of Physics, Vol. 16, 125006, 02.12.2014.

Research output: Contribution to journalArticle

Klimm, F, Borge-Holthoefer, J, Wessel, N, Kurths, J & Zamora-Lopez, G 2014, 'Individual nodes contribution to the mesoscale of complex networks', New Journal of Physics, vol. 16, 125006. https://doi.org/10.1088/1367-2630/16/12/125006
Klimm F, Borge-Holthoefer J, Wessel N, Kurths J, Zamora-Lopez G. Individual nodes contribution to the mesoscale of complex networks. New Journal of Physics. 2014 Dec 2;16. 125006. https://doi.org/10.1088/1367-2630/16/12/125006
Klimm, Florian ; Borge-Holthoefer, Javier ; Wessel, Niels ; Kurths, Jürgen ; Zamora-Lopez, Gorka. / Individual nodes contribution to the mesoscale of complex networks. In: New Journal of Physics. 2014 ; Vol. 16.
@article{3a3c0c11d85840ca9e458c3d8caf30c2,
title = "Individual nodes contribution to the mesoscale of complex networks",
abstract = "The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.",
keywords = "community structure, genetic regulatory networks, network metrics, neuronal networks",
author = "Florian Klimm and Javier Borge-Holthoefer and Niels Wessel and J{\"u}rgen Kurths and Gorka Zamora-Lopez",
year = "2014",
month = "12",
day = "2",
doi = "10.1088/1367-2630/16/12/125006",
language = "English",
volume = "16",
journal = "New Journal of Physics",
issn = "1367-2630",
publisher = "IOP Publishing Ltd.",

}

TY - JOUR

T1 - Individual nodes contribution to the mesoscale of complex networks

AU - Klimm, Florian

AU - Borge-Holthoefer, Javier

AU - Wessel, Niels

AU - Kurths, Jürgen

AU - Zamora-Lopez, Gorka

PY - 2014/12/2

Y1 - 2014/12/2

N2 - The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.

AB - The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.

KW - community structure

KW - genetic regulatory networks

KW - network metrics

KW - neuronal networks

UR - http://www.scopus.com/inward/record.url?scp=84920271336&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84920271336&partnerID=8YFLogxK

U2 - 10.1088/1367-2630/16/12/125006

DO - 10.1088/1367-2630/16/12/125006

M3 - Article

AN - SCOPUS:84920271336

VL - 16

JO - New Journal of Physics

JF - New Journal of Physics

SN - 1367-2630

M1 - 125006

ER -