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Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models

Discovering low-dimensional structure in real-world networks requires a suitable null model that defines the absence of meaningful structure. Here we introduce a spectral approach for detecting a network’s low-dimensional structure, and the nodes that participate in it, using any null model. We use...

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Detalles Bibliográficos
Autores principales: Humphries, Mark D., Caballero, Javier A., Evans, Mat, Maggi, Silvia, Singh, Abhinav
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253422/
https://www.ncbi.nlm.nih.gov/pubmed/34214126
http://dx.doi.org/10.1371/journal.pone.0254057
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author Humphries, Mark D.
Caballero, Javier A.
Evans, Mat
Maggi, Silvia
Singh, Abhinav
author_facet Humphries, Mark D.
Caballero, Javier A.
Evans, Mat
Maggi, Silvia
Singh, Abhinav
author_sort Humphries, Mark D.
collection PubMed
description Discovering low-dimensional structure in real-world networks requires a suitable null model that defines the absence of meaningful structure. Here we introduce a spectral approach for detecting a network’s low-dimensional structure, and the nodes that participate in it, using any null model. We use generative models to estimate the expected eigenvalue distribution under a specified null model, and then detect where the data network’s eigenspectra exceed the estimated bounds. On synthetic networks, this spectral estimation approach cleanly detects transitions between random and community structure, recovers the number and membership of communities, and removes noise nodes. On real networks spectral estimation finds either a significant fraction of noise nodes or no departure from a null model, in stark contrast to traditional community detection methods. Across all analyses, we find the choice of null model can strongly alter conclusions about the presence of network structure. Our spectral estimation approach is therefore a promising basis for detecting low-dimensional structure in real-world networks, or lack thereof.
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spelling pubmed-82534222021-07-13 Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models Humphries, Mark D. Caballero, Javier A. Evans, Mat Maggi, Silvia Singh, Abhinav PLoS One Research Article Discovering low-dimensional structure in real-world networks requires a suitable null model that defines the absence of meaningful structure. Here we introduce a spectral approach for detecting a network’s low-dimensional structure, and the nodes that participate in it, using any null model. We use generative models to estimate the expected eigenvalue distribution under a specified null model, and then detect where the data network’s eigenspectra exceed the estimated bounds. On synthetic networks, this spectral estimation approach cleanly detects transitions between random and community structure, recovers the number and membership of communities, and removes noise nodes. On real networks spectral estimation finds either a significant fraction of noise nodes or no departure from a null model, in stark contrast to traditional community detection methods. Across all analyses, we find the choice of null model can strongly alter conclusions about the presence of network structure. Our spectral estimation approach is therefore a promising basis for detecting low-dimensional structure in real-world networks, or lack thereof. Public Library of Science 2021-07-02 /pmc/articles/PMC8253422/ /pubmed/34214126 http://dx.doi.org/10.1371/journal.pone.0254057 Text en © 2021 Humphries et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Humphries, Mark D.
Caballero, Javier A.
Evans, Mat
Maggi, Silvia
Singh, Abhinav
Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models
title Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models
title_full Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models
title_fullStr Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models
title_full_unstemmed Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models
title_short Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models
title_sort spectral estimation for detecting low-dimensional structure in networks using arbitrary null models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253422/
https://www.ncbi.nlm.nih.gov/pubmed/34214126
http://dx.doi.org/10.1371/journal.pone.0254057
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