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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

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Authors

MARK HUMPHRIES Mark.Humphries@nottingham.ac.uk
Professor of Computational Neuroscience

Javier A. Caballero

Mat Evans

Abhinav Singh



Contributors

Gabriele Oliva
Editor

Abstract

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.

Citation

Humphries, M. D., Caballero, J. A., Evans, M., Maggi, S., & Singh, A. (2021). Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models. PLoS ONE, 16(7), Article e0254057. https://doi.org/10.1371/journal.pone.0254057

Journal Article Type Article
Acceptance Date Jun 24, 2021
Online Publication Date Jul 2, 2021
Publication Date Jul 2, 2021
Deposit Date Jun 29, 2021
Publicly Available Date Jul 5, 2021
Journal PLoS ONE
Electronic ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 16
Issue 7
Article Number e0254057
DOI https://doi.org/10.1371/journal.pone.0254057
Keywords General Biochemistry, Genetics and Molecular Biology; General Agricultural and Biological Sciences; General Medicine
Public URL https://nottingham-repository.worktribe.com/output/5746144
Publisher URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254057

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