A Robust Method for Inferring Network Structures

Abstract Inferring the network structure from limited observable data is significant in molecular biology, communication and many other areas. It is challenging, primarily because the observable data are sparse, finite and noisy. The development of machine learning and network structure study provid...

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Autores principales:	Yang Yang, Tingjin Luo, Zhoujun Li, Xiaoming Zhang, Philip S. Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4e7ffb742756457c981d1f6ec81812c5
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id	oai:doaj.org-article:4e7ffb742756457c981d1f6ec81812c5
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spelling	oai:doaj.org-article:4e7ffb742756457c981d1f6ec81812c52021-12-02T15:06:09ZA Robust Method for Inferring Network Structures10.1038/s41598-017-04725-22045-2322https://doaj.org/article/4e7ffb742756457c981d1f6ec81812c52017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04725-2https://doaj.org/toc/2045-2322Abstract Inferring the network structure from limited observable data is significant in molecular biology, communication and many other areas. It is challenging, primarily because the observable data are sparse, finite and noisy. The development of machine learning and network structure study provides a great chance to solve the problem. In this paper, we propose an iterative smoothing algorithm with structure sparsity (ISSS) method. The elastic penalty in the model is introduced for the sparse solution, identifying group features and avoiding over-fitting, and the total variation (TV) penalty in the model can effectively utilize the structure information to identify the neighborhood of the vertices. Due to the non-smoothness of the elastic and structural TV penalties, an efficient algorithm with the Nesterov’s smoothing optimization technique is proposed to solve the non-smooth problem. The experimental results on both synthetic and real-world networks show that the proposed model is robust against insufficient data and high noise. In addition, we investigate many factors that play important roles in identifying the performance of ISSS.Yang YangTingjin LuoZhoujun LiXiaoming ZhangPhilip S. YuNature 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 Yang Yang Tingjin Luo Zhoujun Li Xiaoming Zhang Philip S. Yu A Robust Method for Inferring Network Structures
description	Abstract Inferring the network structure from limited observable data is significant in molecular biology, communication and many other areas. It is challenging, primarily because the observable data are sparse, finite and noisy. The development of machine learning and network structure study provides a great chance to solve the problem. In this paper, we propose an iterative smoothing algorithm with structure sparsity (ISSS) method. The elastic penalty in the model is introduced for the sparse solution, identifying group features and avoiding over-fitting, and the total variation (TV) penalty in the model can effectively utilize the structure information to identify the neighborhood of the vertices. Due to the non-smoothness of the elastic and structural TV penalties, an efficient algorithm with the Nesterov’s smoothing optimization technique is proposed to solve the non-smooth problem. The experimental results on both synthetic and real-world networks show that the proposed model is robust against insufficient data and high noise. In addition, we investigate many factors that play important roles in identifying the performance of ISSS.
format	article
author	Yang Yang Tingjin Luo Zhoujun Li Xiaoming Zhang Philip S. Yu
author_facet	Yang Yang Tingjin Luo Zhoujun Li Xiaoming Zhang Philip S. Yu
author_sort	Yang Yang
title	A Robust Method for Inferring Network Structures
title_short	A Robust Method for Inferring Network Structures
title_full	A Robust Method for Inferring Network Structures
title_fullStr	A Robust Method for Inferring Network Structures
title_full_unstemmed	A Robust Method for Inferring Network Structures
title_sort	robust method for inferring network structures
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/4e7ffb742756457c981d1f6ec81812c5
work_keys_str_mv	AT yangyang arobustmethodforinferringnetworkstructures AT tingjinluo arobustmethodforinferringnetworkstructures AT zhoujunli arobustmethodforinferringnetworkstructures AT xiaomingzhang arobustmethodforinferringnetworkstructures AT philipsyu arobustmethodforinferringnetworkstructures AT yangyang robustmethodforinferringnetworkstructures AT tingjinluo robustmethodforinferringnetworkstructures AT zhoujunli robustmethodforinferringnetworkstructures AT xiaomingzhang robustmethodforinferringnetworkstructures AT philipsyu robustmethodforinferringnetworkstructures
_version_	1718388581937971200

A Robust Method for Inferring Network Structures

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