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Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications
Through an elegant geometrical interpretation, the multi-fractal analysis quantifies the spatial and temporal irregularities of the structural and dynamical formation of complex networks. Despite its effectiveness in unweighted networks, the multi-fractal geometry of weighted complex networks, the r...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5548933/ https://www.ncbi.nlm.nih.gov/pubmed/28790321 http://dx.doi.org/10.1038/s41598-017-07209-5 |
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author | Xue, Yuankun Bogdan, Paul |
author_facet | Xue, Yuankun Bogdan, Paul |
author_sort | Xue, Yuankun |
collection | PubMed |
description | Through an elegant geometrical interpretation, the multi-fractal analysis quantifies the spatial and temporal irregularities of the structural and dynamical formation of complex networks. Despite its effectiveness in unweighted networks, the multi-fractal geometry of weighted complex networks, the role of interaction intensity, the influence of the embedding metric spaces and the design of reliable estimation algorithms remain open challenges. To address these challenges, we present a set of reliable multi-fractal estimation algorithms for quantifying the structural complexity and heterogeneity of weighted complex networks. Our methodology uncovers that (i) the weights of complex networks and their underlying metric spaces play a key role in dictating the existence of multi-fractal scaling and (ii) the multi-fractal scaling can be localized in both space and scales. In addition, this multi-fractal characterization framework enables the construction of a scaling-based similarity metric and the identification of community structure of human brain connectome. The detected communities are accurately aligned with the biological brain connectivity patterns. This characterization framework has no constraint on the target network and can thus be leveraged as a basis for both structural and dynamic analysis of networks in a wide spectrum of applications. |
format | Online Article Text |
id | pubmed-5548933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55489332017-08-11 Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications Xue, Yuankun Bogdan, Paul Sci Rep Article Through an elegant geometrical interpretation, the multi-fractal analysis quantifies the spatial and temporal irregularities of the structural and dynamical formation of complex networks. Despite its effectiveness in unweighted networks, the multi-fractal geometry of weighted complex networks, the role of interaction intensity, the influence of the embedding metric spaces and the design of reliable estimation algorithms remain open challenges. To address these challenges, we present a set of reliable multi-fractal estimation algorithms for quantifying the structural complexity and heterogeneity of weighted complex networks. Our methodology uncovers that (i) the weights of complex networks and their underlying metric spaces play a key role in dictating the existence of multi-fractal scaling and (ii) the multi-fractal scaling can be localized in both space and scales. In addition, this multi-fractal characterization framework enables the construction of a scaling-based similarity metric and the identification of community structure of human brain connectome. The detected communities are accurately aligned with the biological brain connectivity patterns. This characterization framework has no constraint on the target network and can thus be leveraged as a basis for both structural and dynamic analysis of networks in a wide spectrum of applications. Nature Publishing Group UK 2017-08-08 /pmc/articles/PMC5548933/ /pubmed/28790321 http://dx.doi.org/10.1038/s41598-017-07209-5 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Xue, Yuankun Bogdan, Paul Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications |
title | Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications |
title_full | Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications |
title_fullStr | Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications |
title_full_unstemmed | Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications |
title_short | Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications |
title_sort | reliable multi-fractal characterization of weighted complex networks: algorithms and implications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5548933/ https://www.ncbi.nlm.nih.gov/pubmed/28790321 http://dx.doi.org/10.1038/s41598-017-07209-5 |
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