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SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures
Network measures are used to predict the behavior of different systems. To be able to investigate how various structures behave and interact we need a wide range of theoretical networks to explore. Both spatial and non-spatial methods exist for generating networks but they are limited in the ability...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3411677/ https://www.ncbi.nlm.nih.gov/pubmed/22876329 http://dx.doi.org/10.1371/journal.pone.0042679 |
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author | Lennartsson, Jenny Håkansson, Nina Wennergren, Uno Jonsson, Annie |
author_facet | Lennartsson, Jenny Håkansson, Nina Wennergren, Uno Jonsson, Annie |
author_sort | Lennartsson, Jenny |
collection | PubMed |
description | Network measures are used to predict the behavior of different systems. To be able to investigate how various structures behave and interact we need a wide range of theoretical networks to explore. Both spatial and non-spatial methods exist for generating networks but they are limited in the ability of producing wide range of network structures. We extend an earlier version of a spatial spectral network algorithm to generate a large variety of networks across almost all the theoretical spectra of the following network measures: average clustering coefficient, degree assortativity, fragmentation index, and mean degree. We compare this extended spatial spectral network-generating algorithm with a non-spatial algorithm regarding their ability to create networks with different structures and network measures. The spatial spectral network-generating algorithm can generate networks over a much broader scale than the non-spatial and other known network algorithms. To exemplify the ability to regenerate real networks, we regenerate networks with structures similar to two real Swedish swine transport networks. Results show that the spatial algorithm is an appropriate model with correlation coefficients at 0.99. This novel algorithm can even create negative assortativity and managed to achieve assortativity values that spans over almost the entire theoretical range. |
format | Online Article Text |
id | pubmed-3411677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-34116772012-08-08 SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures Lennartsson, Jenny Håkansson, Nina Wennergren, Uno Jonsson, Annie PLoS One Research Article Network measures are used to predict the behavior of different systems. To be able to investigate how various structures behave and interact we need a wide range of theoretical networks to explore. Both spatial and non-spatial methods exist for generating networks but they are limited in the ability of producing wide range of network structures. We extend an earlier version of a spatial spectral network algorithm to generate a large variety of networks across almost all the theoretical spectra of the following network measures: average clustering coefficient, degree assortativity, fragmentation index, and mean degree. We compare this extended spatial spectral network-generating algorithm with a non-spatial algorithm regarding their ability to create networks with different structures and network measures. The spatial spectral network-generating algorithm can generate networks over a much broader scale than the non-spatial and other known network algorithms. To exemplify the ability to regenerate real networks, we regenerate networks with structures similar to two real Swedish swine transport networks. Results show that the spatial algorithm is an appropriate model with correlation coefficients at 0.99. This novel algorithm can even create negative assortativity and managed to achieve assortativity values that spans over almost the entire theoretical range. Public Library of Science 2012-08-02 /pmc/articles/PMC3411677/ /pubmed/22876329 http://dx.doi.org/10.1371/journal.pone.0042679 Text en © 2012 Lennartsson et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Lennartsson, Jenny Håkansson, Nina Wennergren, Uno Jonsson, Annie SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures |
title | SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures |
title_full | SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures |
title_fullStr | SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures |
title_full_unstemmed | SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures |
title_short | SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures |
title_sort | specnet: a spatial network algorithm that generates a wide range of specific structures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3411677/ https://www.ncbi.nlm.nih.gov/pubmed/22876329 http://dx.doi.org/10.1371/journal.pone.0042679 |
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