Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications

Abstract 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 networ...

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Autores principales:	Yuankun Xue, Paul Bogdan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7a6be4536aa84783a8a4cd7a52efc4d4
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spelling	oai:doaj.org-article:7a6be4536aa84783a8a4cd7a52efc4d42021-12-02T11:40:31ZReliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications10.1038/s41598-017-07209-52045-2322https://doaj.org/article/7a6be4536aa84783a8a4cd7a52efc4d42017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07209-5https://doaj.org/toc/2045-2322Abstract 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.Yuankun XuePaul BogdanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-22 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuankun Xue Paul Bogdan Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications
description	Abstract 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	article
author	Yuankun Xue Paul Bogdan
author_facet	Yuankun Xue Paul Bogdan
author_sort	Yuankun Xue
title	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_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_sort	reliable multi-fractal characterization of weighted complex networks: algorithms and implications
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/7a6be4536aa84783a8a4cd7a52efc4d4
work_keys_str_mv	AT yuankunxue reliablemultifractalcharacterizationofweightedcomplexnetworksalgorithmsandimplications AT paulbogdan reliablemultifractalcharacterizationofweightedcomplexnetworksalgorithmsandimplications
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Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications

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