Modeling Community Evolution Characteristics of Dynamic Networks with Evolutionary Bayesian Nonnegative Matrix Factorization

In most cases, the block structures and evolution characteristics always coexist in dynamic networks. This leads to inaccurate results of temporal community structure analysis with a two-step strategy. Fortunately, a few approaches take the evolution characteristics into account for modeling tempora...

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Autores principales:	Wei Yu, Xiaoming Li, Huaming Wu, Xue Chen, Minghu Tang, Yang Yu, Wenjun Wang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/967e0d188e2a4179a97332f675c613d3
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id	oai:doaj.org-article:967e0d188e2a4179a97332f675c613d3
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spelling	oai:doaj.org-article:967e0d188e2a4179a97332f675c613d32021-11-15T01:19:12ZModeling Community Evolution Characteristics of Dynamic Networks with Evolutionary Bayesian Nonnegative Matrix Factorization1099-052610.1155/2021/7215888https://doaj.org/article/967e0d188e2a4179a97332f675c613d32021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7215888https://doaj.org/toc/1099-0526In most cases, the block structures and evolution characteristics always coexist in dynamic networks. This leads to inaccurate results of temporal community structure analysis with a two-step strategy. Fortunately, a few approaches take the evolution characteristics into account for modeling temporal community structures. But the number of communities cannot be determined automatically. Therefore, a model, Evolutionary Bayesian Nonnegative Matrix Factorization (EvoBNMF), is proposed in this paper. It focuses on modeling the temporal community structures with evolution characteristics. More specifically, the evolution behavior, which is introduced into EvoBNMF, can quantify the transfer intensity of communities between adjacent snapshots for modeling the evolution characteristics. Innovatively, the most appropriate number of communities can be determined autonomously by shrinking the corresponding evolution behaviors. Experimental results show that our approach has superior performance on temporal community detection with the virtue of autonomous determination of the number of communities.Wei YuXiaoming LiHuaming WuXue ChenMinghu TangYang YuWenjun WangHindawi-WileyarticleElectronic computers. Computer scienceQA75.5-76.95ENComplexity, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Wei Yu Xiaoming Li Huaming Wu Xue Chen Minghu Tang Yang Yu Wenjun Wang Modeling Community Evolution Characteristics of Dynamic Networks with Evolutionary Bayesian Nonnegative Matrix Factorization
description	In most cases, the block structures and evolution characteristics always coexist in dynamic networks. This leads to inaccurate results of temporal community structure analysis with a two-step strategy. Fortunately, a few approaches take the evolution characteristics into account for modeling temporal community structures. But the number of communities cannot be determined automatically. Therefore, a model, Evolutionary Bayesian Nonnegative Matrix Factorization (EvoBNMF), is proposed in this paper. It focuses on modeling the temporal community structures with evolution characteristics. More specifically, the evolution behavior, which is introduced into EvoBNMF, can quantify the transfer intensity of communities between adjacent snapshots for modeling the evolution characteristics. Innovatively, the most appropriate number of communities can be determined autonomously by shrinking the corresponding evolution behaviors. Experimental results show that our approach has superior performance on temporal community detection with the virtue of autonomous determination of the number of communities.
format	article
author	Wei Yu Xiaoming Li Huaming Wu Xue Chen Minghu Tang Yang Yu Wenjun Wang
author_facet	Wei Yu Xiaoming Li Huaming Wu Xue Chen Minghu Tang Yang Yu Wenjun Wang
author_sort	Wei Yu
title	Modeling Community Evolution Characteristics of Dynamic Networks with Evolutionary Bayesian Nonnegative Matrix Factorization
title_short	Modeling Community Evolution Characteristics of Dynamic Networks with Evolutionary Bayesian Nonnegative Matrix Factorization
title_full	Modeling Community Evolution Characteristics of Dynamic Networks with Evolutionary Bayesian Nonnegative Matrix Factorization
title_fullStr	Modeling Community Evolution Characteristics of Dynamic Networks with Evolutionary Bayesian Nonnegative Matrix Factorization
title_full_unstemmed	Modeling Community Evolution Characteristics of Dynamic Networks with Evolutionary Bayesian Nonnegative Matrix Factorization
title_sort	modeling community evolution characteristics of dynamic networks with evolutionary bayesian nonnegative matrix factorization
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/967e0d188e2a4179a97332f675c613d3
work_keys_str_mv	AT weiyu modelingcommunityevolutioncharacteristicsofdynamicnetworkswithevolutionarybayesiannonnegativematrixfactorization AT xiaomingli modelingcommunityevolutioncharacteristicsofdynamicnetworkswithevolutionarybayesiannonnegativematrixfactorization AT huamingwu modelingcommunityevolutioncharacteristicsofdynamicnetworkswithevolutionarybayesiannonnegativematrixfactorization AT xuechen modelingcommunityevolutioncharacteristicsofdynamicnetworkswithevolutionarybayesiannonnegativematrixfactorization AT minghutang modelingcommunityevolutioncharacteristicsofdynamicnetworkswithevolutionarybayesiannonnegativematrixfactorization AT yangyu modelingcommunityevolutioncharacteristicsofdynamicnetworkswithevolutionarybayesiannonnegativematrixfactorization AT wenjunwang modelingcommunityevolutioncharacteristicsofdynamicnetworkswithevolutionarybayesiannonnegativematrixfactorization
_version_	1718428997662015488

Modeling Community Evolution Characteristics of Dynamic Networks with Evolutionary Bayesian Nonnegative Matrix Factorization

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