Overlapping community detection in networks based on link partitioning and partitioning around medoids.

In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph emplo...

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Autores principales:	Alexander Ponomarenko, Leonidas Pitsoulis, Marat Shamshetdinov
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/00cba023e70a4cf69fc10301bb2d1509
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spelling	oai:doaj.org-article:00cba023e70a4cf69fc10301bb2d15092021-12-02T20:14:55ZOverlapping community detection in networks based on link partitioning and partitioning around medoids.1932-620310.1371/journal.pone.0255717https://doaj.org/article/00cba023e70a4cf69fc10301bb2d15092021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255717https://doaj.org/toc/1932-6203In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.Alexander PonomarenkoLeonidas PitsoulisMarat ShamshetdinovPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255717 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alexander Ponomarenko Leonidas Pitsoulis Marat Shamshetdinov Overlapping community detection in networks based on link partitioning and partitioning around medoids.
description	In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.
format	article
author	Alexander Ponomarenko Leonidas Pitsoulis Marat Shamshetdinov
author_facet	Alexander Ponomarenko Leonidas Pitsoulis Marat Shamshetdinov
author_sort	Alexander Ponomarenko
title	Overlapping community detection in networks based on link partitioning and partitioning around medoids.
title_short	Overlapping community detection in networks based on link partitioning and partitioning around medoids.
title_full	Overlapping community detection in networks based on link partitioning and partitioning around medoids.
title_fullStr	Overlapping community detection in networks based on link partitioning and partitioning around medoids.
title_full_unstemmed	Overlapping community detection in networks based on link partitioning and partitioning around medoids.
title_sort	overlapping community detection in networks based on link partitioning and partitioning around medoids.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/00cba023e70a4cf69fc10301bb2d1509
work_keys_str_mv	AT alexanderponomarenko overlappingcommunitydetectioninnetworksbasedonlinkpartitioningandpartitioningaroundmedoids AT leonidaspitsoulis overlappingcommunitydetectioninnetworksbasedonlinkpartitioningandpartitioningaroundmedoids AT maratshamshetdinov overlappingcommunitydetectioninnetworksbasedonlinkpartitioningandpartitioningaroundmedoids
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Overlapping community detection in networks based on link partitioning and partitioning around medoids.

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