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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9955123 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |