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Revealing the Hidden Language of Complex Networks
Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3971399/ https://www.ncbi.nlm.nih.gov/pubmed/24686408 http://dx.doi.org/10.1038/srep04547 |
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author | Yaveroğlu, Ömer Nebil Malod-Dognin, Noël Davis, Darren Levnajic, Zoran Janjic, Vuk Karapandza, Rasa Stojmirovic, Aleksandar Pržulj, Nataša |
author_facet | Yaveroğlu, Ömer Nebil Malod-Dognin, Noël Davis, Darren Levnajic, Zoran Janjic, Vuk Karapandza, Rasa Stojmirovic, Aleksandar Pržulj, Nataša |
author_sort | Yaveroğlu, Ömer Nebil |
collection | PubMed |
description | Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists. |
format | Online Article Text |
id | pubmed-3971399 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-39713992014-04-02 Revealing the Hidden Language of Complex Networks Yaveroğlu, Ömer Nebil Malod-Dognin, Noël Davis, Darren Levnajic, Zoran Janjic, Vuk Karapandza, Rasa Stojmirovic, Aleksandar Pržulj, Nataša Sci Rep Article Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists. Nature Publishing Group 2014-04-01 /pmc/articles/PMC3971399/ /pubmed/24686408 http://dx.doi.org/10.1038/srep04547 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. The images in this article are included in the article's Creative Commons license, unless indicated otherwise in the image credit; if the image is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the image. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ |
spellingShingle | Article Yaveroğlu, Ömer Nebil Malod-Dognin, Noël Davis, Darren Levnajic, Zoran Janjic, Vuk Karapandza, Rasa Stojmirovic, Aleksandar Pržulj, Nataša Revealing the Hidden Language of Complex Networks |
title | Revealing the Hidden Language of Complex Networks |
title_full | Revealing the Hidden Language of Complex Networks |
title_fullStr | Revealing the Hidden Language of Complex Networks |
title_full_unstemmed | Revealing the Hidden Language of Complex Networks |
title_short | Revealing the Hidden Language of Complex Networks |
title_sort | revealing the hidden language of complex networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3971399/ https://www.ncbi.nlm.nih.gov/pubmed/24686408 http://dx.doi.org/10.1038/srep04547 |
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