Node Attribute-enhanced Community Detection in Complex Networks

Abstract Community detection involves grouping the nodes of a network such that nodes in the same community are more densely connected to each other than to the rest of the network. Previous studies have focused mainly on identifying communities in networks using node connectivity. However, each nod...

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Autores principales:	Caiyan Jia, Yafang Li, Matthew B. Carson, Xiaoyang Wang, Jian Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/44cf6d4703b5459f8b38999bff3487bd
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id	oai:doaj.org-article:44cf6d4703b5459f8b38999bff3487bd
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spelling	oai:doaj.org-article:44cf6d4703b5459f8b38999bff3487bd2021-12-02T16:08:00ZNode Attribute-enhanced Community Detection in Complex Networks10.1038/s41598-017-02751-82045-2322https://doaj.org/article/44cf6d4703b5459f8b38999bff3487bd2017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02751-8https://doaj.org/toc/2045-2322Abstract Community detection involves grouping the nodes of a network such that nodes in the same community are more densely connected to each other than to the rest of the network. Previous studies have focused mainly on identifying communities in networks using node connectivity. However, each node in a network may be associated with many attributes. Identifying communities in networks combining node attributes has become increasingly popular in recent years. Most existing methods operate on networks with attributes of binary, categorical, or numerical type only. In this study, we introduce kNN-enhance, a simple and flexible community detection approach that uses node attribute enhancement. This approach adds the k Nearest Neighbor (kNN) graph of node attributes to alleviate the sparsity and the noise effect of an original network, thereby strengthening the community structure in the network. We use two testing algorithms, kNN-nearest and kNN-Kmeans, to partition the newly generated, attribute-enhanced graph. Our analyses of synthetic and real world networks have shown that the proposed algorithms achieve better performance compared to existing state-of-the-art algorithms. Further, the algorithms are able to deal with networks containing different combinations of binary, categorical, or numerical attributes and could be easily extended to the analysis of massive networks.Caiyan JiaYafang LiMatthew B. CarsonXiaoyang WangJian YuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-15 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Caiyan Jia Yafang Li Matthew B. Carson Xiaoyang Wang Jian Yu Node Attribute-enhanced Community Detection in Complex Networks
description	Abstract Community detection involves grouping the nodes of a network such that nodes in the same community are more densely connected to each other than to the rest of the network. Previous studies have focused mainly on identifying communities in networks using node connectivity. However, each node in a network may be associated with many attributes. Identifying communities in networks combining node attributes has become increasingly popular in recent years. Most existing methods operate on networks with attributes of binary, categorical, or numerical type only. In this study, we introduce kNN-enhance, a simple and flexible community detection approach that uses node attribute enhancement. This approach adds the k Nearest Neighbor (kNN) graph of node attributes to alleviate the sparsity and the noise effect of an original network, thereby strengthening the community structure in the network. We use two testing algorithms, kNN-nearest and kNN-Kmeans, to partition the newly generated, attribute-enhanced graph. Our analyses of synthetic and real world networks have shown that the proposed algorithms achieve better performance compared to existing state-of-the-art algorithms. Further, the algorithms are able to deal with networks containing different combinations of binary, categorical, or numerical attributes and could be easily extended to the analysis of massive networks.
format	article
author	Caiyan Jia Yafang Li Matthew B. Carson Xiaoyang Wang Jian Yu
author_facet	Caiyan Jia Yafang Li Matthew B. Carson Xiaoyang Wang Jian Yu
author_sort	Caiyan Jia
title	Node Attribute-enhanced Community Detection in Complex Networks
title_short	Node Attribute-enhanced Community Detection in Complex Networks
title_full	Node Attribute-enhanced Community Detection in Complex Networks
title_fullStr	Node Attribute-enhanced Community Detection in Complex Networks
title_full_unstemmed	Node Attribute-enhanced Community Detection in Complex Networks
title_sort	node attribute-enhanced community detection in complex networks
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/44cf6d4703b5459f8b38999bff3487bd
work_keys_str_mv	AT caiyanjia nodeattributeenhancedcommunitydetectionincomplexnetworks AT yafangli nodeattributeenhancedcommunitydetectionincomplexnetworks AT matthewbcarson nodeattributeenhancedcommunitydetectionincomplexnetworks AT xiaoyangwang nodeattributeenhancedcommunitydetectionincomplexnetworks AT jianyu nodeattributeenhancedcommunitydetectionincomplexnetworks
_version_	1718384651628707840

Node Attribute-enhanced Community Detection in Complex Networks

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