Individual nodeʼs contribution to the mesoscale of complex networks
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2014-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/16/12/125006 |
| _version_ | 1797751414669180928 |
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| author | Florian Klimm Javier Borge-Holthoefer Niels Wessel Jürgen Kurths Gorka Zamora-López |
| author_facet | Florian Klimm Javier Borge-Holthoefer Niels Wessel Jürgen Kurths Gorka Zamora-López |
| author_sort | Florian Klimm |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T16:49:11Z |
| format | Article |
| id | doaj.art-0593edeb444f42f5a7b94e8a9716bb86 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:49:11Z |
| publishDate | 2014-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-0593edeb444f42f5a7b94e8a9716bb862023-08-08T11:25:49ZengIOP PublishingNew Journal of Physics1367-26302014-01-01161212500610.1088/1367-2630/16/12/125006Individual nodeʼs contribution to the mesoscale of complex networksFlorian Klimm0Javier Borge-Holthoefer1Niels Wessel2Jürgen Kurths3Gorka Zamora-López4Department of Physics, Humboldt-Universität zu Berlin , Berlin, Germany; Potsdam Institute for Climate Impact Research, Potsdam , Germany; Doctoral Training Centre, University of Oxford, Oxford , United KingdomQatar Computing Research Institute , Doha, QatarDepartment of Physics, Humboldt-Universität zu Berlin , Berlin, GermanyDepartment of Physics, Humboldt-Universität zu Berlin , Berlin, Germany; Potsdam Institute for Climate Impact Research, Potsdam , Germany; Institute for Complex Systems and Mathematical Biology, University of Aberdeen , Aberdeen, UK; Department of Control Theory, Nizhny Novgorod State University, Nizhny Novgorod 603950, RussiaCenter for Brain and Cognition, Universitat Pompeu Fabra , Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra , Barcelona, SpainThe 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.https://doi.org/10.1088/1367-2630/16/12/125006network metricscommunity structureneuronal networksgenetic regulatory networks87.18.Sn87.18.Cf |
| spellingShingle | Florian Klimm Javier Borge-Holthoefer Niels Wessel Jürgen Kurths Gorka Zamora-López Individual nodeʼs contribution to the mesoscale of complex networks New Journal of Physics network metrics community structure neuronal networks genetic regulatory networks 87.18.Sn 87.18.Cf |
| title | Individual nodeʼs contribution to the mesoscale of complex networks |
| title_full | Individual nodeʼs contribution to the mesoscale of complex networks |
| title_fullStr | Individual nodeʼs contribution to the mesoscale of complex networks |
| title_full_unstemmed | Individual nodeʼs contribution to the mesoscale of complex networks |
| title_short | Individual nodeʼs contribution to the mesoscale of complex networks |
| title_sort | individual node s contribution to the mesoscale of complex networks |
| topic | network metrics community structure neuronal networks genetic regulatory networks 87.18.Sn 87.18.Cf |
| url | https://doi.org/10.1088/1367-2630/16/12/125006 |
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