Link predication based on matrix factorization by fusion of multi class organizations of the network

Abstract Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in ot...

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Autores principales:	Pengfei Jiao, Fei Cai, Yiding Feng, Wenjun Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f71128510edd4c2ca7a40b842a3cb81c
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spelling	oai:doaj.org-article:f71128510edd4c2ca7a40b842a3cb81c2021-12-02T16:06:18ZLink predication based on matrix factorization by fusion of multi class organizations of the network10.1038/s41598-017-09081-92045-2322https://doaj.org/article/f71128510edd4c2ca7a40b842a3cb81c2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-09081-9https://doaj.org/toc/2045-2322Abstract Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix factorization, which is called NMF 3 here. We first map the observed network into another space by kernel functions, which could get the different order organizations. Then we combine the adjacency matrix of the network with one of other organizations, which makes us obtain the objective function of our framework for link predication based on the nonnegative matrix factorization. Third, we derive an iterative algorithm to optimize the objective function, which converges to a local optimum, and we propose a fast optimization strategy for large networks. Lastly, we test the proposed framework based on two kernel functions on a series of real world networks under different sizes of training set, and the experimental results show the feasibility, effectiveness, and competitiveness of the proposed framework.Pengfei JiaoFei CaiYiding FengWenjun WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pengfei Jiao Fei Cai Yiding Feng Wenjun Wang Link predication based on matrix factorization by fusion of multi class organizations of the network
description	Abstract Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix factorization, which is called NMF 3 here. We first map the observed network into another space by kernel functions, which could get the different order organizations. Then we combine the adjacency matrix of the network with one of other organizations, which makes us obtain the objective function of our framework for link predication based on the nonnegative matrix factorization. Third, we derive an iterative algorithm to optimize the objective function, which converges to a local optimum, and we propose a fast optimization strategy for large networks. Lastly, we test the proposed framework based on two kernel functions on a series of real world networks under different sizes of training set, and the experimental results show the feasibility, effectiveness, and competitiveness of the proposed framework.
format	article
author	Pengfei Jiao Fei Cai Yiding Feng Wenjun Wang
author_facet	Pengfei Jiao Fei Cai Yiding Feng Wenjun Wang
author_sort	Pengfei Jiao
title	Link predication based on matrix factorization by fusion of multi class organizations of the network
title_short	Link predication based on matrix factorization by fusion of multi class organizations of the network
title_full	Link predication based on matrix factorization by fusion of multi class organizations of the network
title_fullStr	Link predication based on matrix factorization by fusion of multi class organizations of the network
title_full_unstemmed	Link predication based on matrix factorization by fusion of multi class organizations of the network
title_sort	link predication based on matrix factorization by fusion of multi class organizations of the network
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/f71128510edd4c2ca7a40b842a3cb81c
work_keys_str_mv	AT pengfeijiao linkpredicationbasedonmatrixfactorizationbyfusionofmulticlassorganizationsofthenetwork AT feicai linkpredicationbasedonmatrixfactorizationbyfusionofmulticlassorganizationsofthenetwork AT yidingfeng linkpredicationbasedonmatrixfactorizationbyfusionofmulticlassorganizationsofthenetwork AT wenjunwang linkpredicationbasedonmatrixfactorizationbyfusionofmulticlassorganizationsofthenetwork
_version_	1718385064425816064

Link predication based on matrix factorization by fusion of multi class organizations of the network

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