Abstract
| Original language | English |
|---|---|
| Article number | 125006 |
| Journal | New Journal of Physics |
| Volume | 16 |
| DOIs | |
| Publication status | Published - 2 Dec 2014 |
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Individual nodeʼs contribution to the mesoscale of complex networks. / Klimm, Florian; Borge-Holthoefer, Javier; Wessels, Niels; Kurths, Jurgen; Zamora-Lopez, Gorka.
In: New Journal of Physics, Vol. 16, 125006, 02.12.2014.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Individual nodeʼs contribution to the mesoscale of complex networks
AU - Klimm, Florian
AU - Borge-Holthoefer, Javier
AU - Wessels, Niels
AU - Kurths, Jurgen
AU - Zamora-Lopez, Gorka
N1 - Date of Acceptance: 06/11/2014 We are thankful to Prof Alex Arenas, Dr Sergio Gómez, Veronika Stolbova and Dominik Traxl for their helpful comments. We also thank Joaquín Sanz Remón for kindly providing the data of the Tuberculosis RT network and for his valuable comments. This work has been supported by (JK) the German Federal Ministry of Education and Research (Bernstein Center II, grant no. 01GQ1001A), (FK) the Engineering and Physical Sciences Research Council, and (GZL) the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement number PIEF- GA-2012-331800.
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.
U2 - 10.1088/1367-2630/16/12/125006
DO - 10.1088/1367-2630/16/12/125006
M3 - Article
VL - 16
JO - New Journal of Physics
JF - New Journal of Physics
SN - 1367-2630
M1 - 125006
ER -