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...

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Autores principales:	Mark D Humphries, Javier A Caballero, Mat Evans, Silvia Maggi, Abhinav Singh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8e3af9a39a9346388a00ba72b8347795
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spelling	oai:doaj.org-article:8e3af9a39a9346388a00ba72b83477952021-12-02T20:09:41ZSpectral estimation for detecting low-dimensional structure in networks using arbitrary null models.1932-620310.1371/journal.pone.0254057https://doaj.org/article/8e3af9a39a9346388a00ba72b83477952021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254057https://doaj.org/toc/1932-6203Discovering 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.Mark D HumphriesJavier A CaballeroMat EvansSilvia MaggiAbhinav SinghPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254057 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mark D Humphries Javier A Caballero Mat Evans Silvia Maggi Abhinav Singh Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models.
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.
format	article
author	Mark D Humphries Javier A Caballero Mat Evans Silvia Maggi Abhinav Singh
author_facet	Mark D Humphries Javier A Caballero Mat Evans Silvia Maggi Abhinav Singh
author_sort	Mark D Humphries
title	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_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_sort	spectral estimation for detecting low-dimensional structure in networks using arbitrary null models.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/8e3af9a39a9346388a00ba72b8347795
work_keys_str_mv	AT markdhumphries spectralestimationfordetectinglowdimensionalstructureinnetworksusingarbitrarynullmodels AT javieracaballero spectralestimationfordetectinglowdimensionalstructureinnetworksusingarbitrarynullmodels AT matevans spectralestimationfordetectinglowdimensionalstructureinnetworksusingarbitrarynullmodels AT silviamaggi spectralestimationfordetectinglowdimensionalstructureinnetworksusingarbitrarynullmodels AT abhinavsingh spectralestimationfordetectinglowdimensionalstructureinnetworksusingarbitrarynullmodels
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Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models.

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