Penalized homophily latent space models for directed scale-free networks.

Online social networks like Twitter and Facebook are among the most popular sites on the Internet. Most online social networks involve some specific features, including reciprocity, transitivity and degree heterogeneity. Such networks are so called scale-free networks and have drawn lots of attentio...

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Autores principales:	Hanxuan Yang, Wei Xiong, Xueliang Zhang, Kai Wang, Maozai Tian
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0d2e8c68ab6b4e039ba077388dac998f
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id	oai:doaj.org-article:0d2e8c68ab6b4e039ba077388dac998f
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spelling	oai:doaj.org-article:0d2e8c68ab6b4e039ba077388dac998f2021-12-02T20:15:20ZPenalized homophily latent space models for directed scale-free networks.1932-620310.1371/journal.pone.0253873https://doaj.org/article/0d2e8c68ab6b4e039ba077388dac998f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253873https://doaj.org/toc/1932-6203Online social networks like Twitter and Facebook are among the most popular sites on the Internet. Most online social networks involve some specific features, including reciprocity, transitivity and degree heterogeneity. Such networks are so called scale-free networks and have drawn lots of attention in research. The aim of this paper is to develop a novel methodology for directed network embedding within the latent space model (LSM) framework. It is known, the link probability between two individuals may increase as the features of each become similar, which is referred to as homophily attributes. To this end, penalized pair-specific attributes, acting as a distance measure, are introduced to provide with more powerful interpretation and improve link prediction accuracy, named penalized homophily latent space models (PHLSM). The proposed models also involve in-degree heterogeneity of directed scale-free networks by embedding with the popularity scales. We also introduce LASSO-based PHLSM to produce an accurate and sparse model for high-dimensional covariates. We make Bayesian inference using MCMC algorithms. The finite sample performance of the proposed models is evaluated by three benchmark simulation datasets and two real data examples. Our methods are competitive and interpretable, they outperform existing approaches for fitting directed networks.Hanxuan YangWei XiongXueliang ZhangKai WangMaozai TianPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0253873 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hanxuan Yang Wei Xiong Xueliang Zhang Kai Wang Maozai Tian Penalized homophily latent space models for directed scale-free networks.
description	Online social networks like Twitter and Facebook are among the most popular sites on the Internet. Most online social networks involve some specific features, including reciprocity, transitivity and degree heterogeneity. Such networks are so called scale-free networks and have drawn lots of attention in research. The aim of this paper is to develop a novel methodology for directed network embedding within the latent space model (LSM) framework. It is known, the link probability between two individuals may increase as the features of each become similar, which is referred to as homophily attributes. To this end, penalized pair-specific attributes, acting as a distance measure, are introduced to provide with more powerful interpretation and improve link prediction accuracy, named penalized homophily latent space models (PHLSM). The proposed models also involve in-degree heterogeneity of directed scale-free networks by embedding with the popularity scales. We also introduce LASSO-based PHLSM to produce an accurate and sparse model for high-dimensional covariates. We make Bayesian inference using MCMC algorithms. The finite sample performance of the proposed models is evaluated by three benchmark simulation datasets and two real data examples. Our methods are competitive and interpretable, they outperform existing approaches for fitting directed networks.
format	article
author	Hanxuan Yang Wei Xiong Xueliang Zhang Kai Wang Maozai Tian
author_facet	Hanxuan Yang Wei Xiong Xueliang Zhang Kai Wang Maozai Tian
author_sort	Hanxuan Yang
title	Penalized homophily latent space models for directed scale-free networks.
title_short	Penalized homophily latent space models for directed scale-free networks.
title_full	Penalized homophily latent space models for directed scale-free networks.
title_fullStr	Penalized homophily latent space models for directed scale-free networks.
title_full_unstemmed	Penalized homophily latent space models for directed scale-free networks.
title_sort	penalized homophily latent space models for directed scale-free networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/0d2e8c68ab6b4e039ba077388dac998f
work_keys_str_mv	AT hanxuanyang penalizedhomophilylatentspacemodelsfordirectedscalefreenetworks AT weixiong penalizedhomophilylatentspacemodelsfordirectedscalefreenetworks AT xueliangzhang penalizedhomophilylatentspacemodelsfordirectedscalefreenetworks AT kaiwang penalizedhomophilylatentspacemodelsfordirectedscalefreenetworks AT maozaitian penalizedhomophilylatentspacemodelsfordirectedscalefreenetworks
_version_	1718374566375456768

Penalized homophily latent space models for directed scale-free networks.

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