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Hierarchy Depth in Directed Networks

In this study, we explore the depth measures for flow hierarchy in directed networks. Two simple measures are defined—rooted depth and relative depth—and their properties are discussed. The method of loop collapse is introduced, allowing investigation of networks containing directed cycles. The beha...

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Detalles Bibliográficos
Autores principales: Suchecki, Krzysztof, Hołyst, Janusz A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871166/
https://www.ncbi.nlm.nih.gov/pubmed/35205546
http://dx.doi.org/10.3390/e24020252
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author Suchecki, Krzysztof
Hołyst, Janusz A.
author_facet Suchecki, Krzysztof
Hołyst, Janusz A.
author_sort Suchecki, Krzysztof
collection PubMed
description In this study, we explore the depth measures for flow hierarchy in directed networks. Two simple measures are defined—rooted depth and relative depth—and their properties are discussed. The method of loop collapse is introduced, allowing investigation of networks containing directed cycles. The behavior of the two depth measures is investigated in Erdös-Rényi random graphs, directed Barabási-Albert networks, and in Gnutella p2p share network. A clear distinction in the behavior between non-hierarchical and hierarchical networks is found, with random graphs featuring unimodal distribution of depths dependent on arc density, while for hierarchical systems the distributions are similar for different network densities. Relative depth shows the same behavior as existing trophic level measure for tree-like networks, but is only statistically correlated for more complex topologies, including acyclic directed graphs.
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spelling pubmed-88711662022-02-25 Hierarchy Depth in Directed Networks Suchecki, Krzysztof Hołyst, Janusz A. Entropy (Basel) Article In this study, we explore the depth measures for flow hierarchy in directed networks. Two simple measures are defined—rooted depth and relative depth—and their properties are discussed. The method of loop collapse is introduced, allowing investigation of networks containing directed cycles. The behavior of the two depth measures is investigated in Erdös-Rényi random graphs, directed Barabási-Albert networks, and in Gnutella p2p share network. A clear distinction in the behavior between non-hierarchical and hierarchical networks is found, with random graphs featuring unimodal distribution of depths dependent on arc density, while for hierarchical systems the distributions are similar for different network densities. Relative depth shows the same behavior as existing trophic level measure for tree-like networks, but is only statistically correlated for more complex topologies, including acyclic directed graphs. MDPI 2022-02-08 /pmc/articles/PMC8871166/ /pubmed/35205546 http://dx.doi.org/10.3390/e24020252 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Suchecki, Krzysztof
Hołyst, Janusz A.
Hierarchy Depth in Directed Networks
title Hierarchy Depth in Directed Networks
title_full Hierarchy Depth in Directed Networks
title_fullStr Hierarchy Depth in Directed Networks
title_full_unstemmed Hierarchy Depth in Directed Networks
title_short Hierarchy Depth in Directed Networks
title_sort hierarchy depth in directed networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871166/
https://www.ncbi.nlm.nih.gov/pubmed/35205546
http://dx.doi.org/10.3390/e24020252
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