Least-squares community extraction in feature-rich networks using similarity data.

We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and int...

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Autores principales:	Soroosh Shalileh, Boris Mirkin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d93a00a917b64d39a6a3f6d18cadbc80
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id	oai:doaj.org-article:d93a00a917b64d39a6a3f6d18cadbc80
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spelling	oai:doaj.org-article:d93a00a917b64d39a6a3f6d18cadbc802021-12-02T20:09:14ZLeast-squares community extraction in feature-rich networks using similarity data.1932-620310.1371/journal.pone.0254377https://doaj.org/article/d93a00a917b64d39a6a3f6d18cadbc802021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254377https://doaj.org/toc/1932-6203We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least-squares additive criterion allows us to search for communities one-by-one and to find each community by adding entities one by one. A focus of this paper is that the feature-space data part is converted into a similarity matrix format. The similarity/link values can be used in either of two modes: (a) as measured in the same scale so that one may can meaningfully compare and sum similarity values across the entire similarity matrix (summability mode), and (b) similarity values in one column should not be compared with the values in other columns (nonsummability mode). The two input matrices and two modes lead us to developing four different Iterative Community Extraction from Similarity data (ICESi) algorithms, which determine the number of communities automatically. Our experiments at real-world and synthetic datasets show that these algorithms are valid and competitive.Soroosh ShalilehBoris MirkinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254377 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Soroosh Shalileh Boris Mirkin Least-squares community extraction in feature-rich networks using similarity data.
description	We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least-squares additive criterion allows us to search for communities one-by-one and to find each community by adding entities one by one. A focus of this paper is that the feature-space data part is converted into a similarity matrix format. The similarity/link values can be used in either of two modes: (a) as measured in the same scale so that one may can meaningfully compare and sum similarity values across the entire similarity matrix (summability mode), and (b) similarity values in one column should not be compared with the values in other columns (nonsummability mode). The two input matrices and two modes lead us to developing four different Iterative Community Extraction from Similarity data (ICESi) algorithms, which determine the number of communities automatically. Our experiments at real-world and synthetic datasets show that these algorithms are valid and competitive.
format	article
author	Soroosh Shalileh Boris Mirkin
author_facet	Soroosh Shalileh Boris Mirkin
author_sort	Soroosh Shalileh
title	Least-squares community extraction in feature-rich networks using similarity data.
title_short	Least-squares community extraction in feature-rich networks using similarity data.
title_full	Least-squares community extraction in feature-rich networks using similarity data.
title_fullStr	Least-squares community extraction in feature-rich networks using similarity data.
title_full_unstemmed	Least-squares community extraction in feature-rich networks using similarity data.
title_sort	least-squares community extraction in feature-rich networks using similarity data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d93a00a917b64d39a6a3f6d18cadbc80
work_keys_str_mv	AT sorooshshalileh leastsquarescommunityextractioninfeaturerichnetworksusingsimilaritydata AT borismirkin leastsquarescommunityextractioninfeaturerichnetworksusingsimilaritydata
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Least-squares community extraction in feature-rich networks using similarity data.

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