A generalised significance test for individual communities in networks

Abstract Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various asp...

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Autores principales:	Sadamori Kojaku, Naoki Masuda
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b4ea965928db49e5a29d0ebbea1c97f5
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spelling	oai:doaj.org-article:b4ea965928db49e5a29d0ebbea1c97f52021-12-02T16:07:52ZA generalised significance test for individual communities in networks10.1038/s41598-018-25560-z2045-2322https://doaj.org/article/b4ea965928db49e5a29d0ebbea1c97f52018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25560-zhttps://doaj.org/toc/2045-2322Abstract Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a given network. Compared to the previous methods, the present algorithm is unique in that it accepts different community-detection algorithms and the corresponding quality function for single communities. The present method requires that a quality of each community can be quantified and that community detection is performed as optimisation of such a quality function summed over the communities. Various community detection algorithms including modularity maximisation and graph partitioning meet this criterion. Our method estimates a distribution of the quality function for randomised networks to calculate a likelihood of each community in the given network. We illustrate our algorithm by synthetic and empirical networks.Sadamori KojakuNaoki MasudaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sadamori Kojaku Naoki Masuda A generalised significance test for individual communities in networks
description	Abstract Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a given network. Compared to the previous methods, the present algorithm is unique in that it accepts different community-detection algorithms and the corresponding quality function for single communities. The present method requires that a quality of each community can be quantified and that community detection is performed as optimisation of such a quality function summed over the communities. Various community detection algorithms including modularity maximisation and graph partitioning meet this criterion. Our method estimates a distribution of the quality function for randomised networks to calculate a likelihood of each community in the given network. We illustrate our algorithm by synthetic and empirical networks.
format	article
author	Sadamori Kojaku Naoki Masuda
author_facet	Sadamori Kojaku Naoki Masuda
author_sort	Sadamori Kojaku
title	A generalised significance test for individual communities in networks
title_short	A generalised significance test for individual communities in networks
title_full	A generalised significance test for individual communities in networks
title_fullStr	A generalised significance test for individual communities in networks
title_full_unstemmed	A generalised significance test for individual communities in networks
title_sort	generalised significance test for individual communities in networks
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/b4ea965928db49e5a29d0ebbea1c97f5
work_keys_str_mv	AT sadamorikojaku ageneralisedsignificancetestforindividualcommunitiesinnetworks AT naokimasuda ageneralisedsignificancetestforindividualcommunitiesinnetworks AT sadamorikojaku generalisedsignificancetestforindividualcommunitiesinnetworks AT naokimasuda generalisedsignificancetestforindividualcommunitiesinnetworks
_version_	1718384680283144192

A generalised significance test for individual communities in networks

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