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Analysing the sensitivity of nestedness detection methods

Many bipartite and unipartite real-world networks display a nested structure. Examples pervade different disciplines: biological ecosystems (e.g. mutualistic networks), economic networks (e.g. manufactures and contractors networks) to financial networks (e.g. bank lending networks), etc. A nested ne...

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Detalles Bibliográficos
Autores principales: Grimm, Alexander, Tessone, Claudio J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214258/
https://www.ncbi.nlm.nih.gov/pubmed/30443590
http://dx.doi.org/10.1007/s41109-017-0057-9
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author Grimm, Alexander
Tessone, Claudio J.
author_facet Grimm, Alexander
Tessone, Claudio J.
author_sort Grimm, Alexander
collection PubMed
description Many bipartite and unipartite real-world networks display a nested structure. Examples pervade different disciplines: biological ecosystems (e.g. mutualistic networks), economic networks (e.g. manufactures and contractors networks) to financial networks (e.g. bank lending networks), etc. A nested network has a topology such that a vertex’s neighbourhood contains the neighbourhood of vertices of lower degree; thus –upon vertex reordering– the adjacency matrix is step-wise. Despite its strict mathematical definition and the interest triggered by their common occurrence, it is not easy to measure the extent of nested graphs unequivocally. Among others, there exist three methods for detection and quantification of nestedness that are widely used: BINMATNEST, NODF, and fitness-complexity metric (FCM). However, these methods fail in assessing the existence of nestedness for graphs of low (NODF) and high (NODF, BINMATNEST) network density. Another common shortcoming of these approaches is the underlying assumption that all vertices belong to a nested component. However, many real-world networks have solely a sub-component (i.e. a subset of its vertices) that is nested. Thus, unveiling which vertices pertain to the nested component is an important research question, unaddressed by the methods available so far. In this contribution, we study in detail the algorithm Nestedness detection based on Local Neighbourhood (NESTLON). This algorithm resorts solely on local information and detects nestedness on a broad range of nested graphs independently of their nature and density. Further, we introduce a benchmark model that allows us to tune the degree of nestedness in a controlled manner and study the performance of different algorithms. Our results show that NESTLON outperforms both BINMATNEST and NODF.
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spelling pubmed-62142582018-11-13 Analysing the sensitivity of nestedness detection methods Grimm, Alexander Tessone, Claudio J. Appl Netw Sci Research Many bipartite and unipartite real-world networks display a nested structure. Examples pervade different disciplines: biological ecosystems (e.g. mutualistic networks), economic networks (e.g. manufactures and contractors networks) to financial networks (e.g. bank lending networks), etc. A nested network has a topology such that a vertex’s neighbourhood contains the neighbourhood of vertices of lower degree; thus –upon vertex reordering– the adjacency matrix is step-wise. Despite its strict mathematical definition and the interest triggered by their common occurrence, it is not easy to measure the extent of nested graphs unequivocally. Among others, there exist three methods for detection and quantification of nestedness that are widely used: BINMATNEST, NODF, and fitness-complexity metric (FCM). However, these methods fail in assessing the existence of nestedness for graphs of low (NODF) and high (NODF, BINMATNEST) network density. Another common shortcoming of these approaches is the underlying assumption that all vertices belong to a nested component. However, many real-world networks have solely a sub-component (i.e. a subset of its vertices) that is nested. Thus, unveiling which vertices pertain to the nested component is an important research question, unaddressed by the methods available so far. In this contribution, we study in detail the algorithm Nestedness detection based on Local Neighbourhood (NESTLON). This algorithm resorts solely on local information and detects nestedness on a broad range of nested graphs independently of their nature and density. Further, we introduce a benchmark model that allows us to tune the degree of nestedness in a controlled manner and study the performance of different algorithms. Our results show that NESTLON outperforms both BINMATNEST and NODF. Springer International Publishing 2017-10-23 2017 /pmc/articles/PMC6214258/ /pubmed/30443590 http://dx.doi.org/10.1007/s41109-017-0057-9 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.
spellingShingle Research
Grimm, Alexander
Tessone, Claudio J.
Analysing the sensitivity of nestedness detection methods
title Analysing the sensitivity of nestedness detection methods
title_full Analysing the sensitivity of nestedness detection methods
title_fullStr Analysing the sensitivity of nestedness detection methods
title_full_unstemmed Analysing the sensitivity of nestedness detection methods
title_short Analysing the sensitivity of nestedness detection methods
title_sort analysing the sensitivity of nestedness detection methods
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214258/
https://www.ncbi.nlm.nih.gov/pubmed/30443590
http://dx.doi.org/10.1007/s41109-017-0057-9
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