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Estimating Mixed Memberships in Directed Networks by Spectral Clustering

Community detection is an important and powerful way to understand the latent structure of complex networks in social network analysis. This paper considers the problem of estimating community memberships of nodes in a directed network, where a node may belong to multiple communities. For such a dir...

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Detalles Bibliográficos
Autor principal: Qing, Huan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955123/
https://www.ncbi.nlm.nih.gov/pubmed/36832711
http://dx.doi.org/10.3390/e25020345
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author Qing, Huan
author_facet Qing, Huan
author_sort Qing, Huan
collection PubMed
description Community detection is an important and powerful way to understand the latent structure of complex networks in social network analysis. This paper considers the problem of estimating community memberships of nodes in a directed network, where a node may belong to multiple communities. For such a directed network, existing models either assume that each node belongs solely to one community or ignore variation in node degree. Here, a directed degree corrected mixed membership (DiDCMM) model is proposed by considering degree heterogeneity. An efficient spectral clustering algorithm with a theoretical guarantee of consistent estimation is designed to fit DiDCMM. We apply our algorithm to a small scale of computer-generated directed networks and several real-world directed networks.
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spelling pubmed-99551232023-02-25 Estimating Mixed Memberships in Directed Networks by Spectral Clustering Qing, Huan Entropy (Basel) Article Community detection is an important and powerful way to understand the latent structure of complex networks in social network analysis. This paper considers the problem of estimating community memberships of nodes in a directed network, where a node may belong to multiple communities. For such a directed network, existing models either assume that each node belongs solely to one community or ignore variation in node degree. Here, a directed degree corrected mixed membership (DiDCMM) model is proposed by considering degree heterogeneity. An efficient spectral clustering algorithm with a theoretical guarantee of consistent estimation is designed to fit DiDCMM. We apply our algorithm to a small scale of computer-generated directed networks and several real-world directed networks. MDPI 2023-02-13 /pmc/articles/PMC9955123/ /pubmed/36832711 http://dx.doi.org/10.3390/e25020345 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Qing, Huan
Estimating Mixed Memberships in Directed Networks by Spectral Clustering
title Estimating Mixed Memberships in Directed Networks by Spectral Clustering
title_full Estimating Mixed Memberships in Directed Networks by Spectral Clustering
title_fullStr Estimating Mixed Memberships in Directed Networks by Spectral Clustering
title_full_unstemmed Estimating Mixed Memberships in Directed Networks by Spectral Clustering
title_short Estimating Mixed Memberships in Directed Networks by Spectral Clustering
title_sort estimating mixed memberships in directed networks by spectral clustering
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955123/
https://www.ncbi.nlm.nih.gov/pubmed/36832711
http://dx.doi.org/10.3390/e25020345
work_keys_str_mv AT qinghuan estimatingmixedmembershipsindirectednetworksbyspectralclustering