Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality

Abstract Reconstruction of networks underlying complex systems is one of the most crucial problems in many areas of engineering and science. In this paper, rather than identifying parameters of complex systems governed by pre-defined models or taking some polynomial and rational functions as a prior...

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Autores principales:	Guanxue Yang, Lin Wang, Xiaofan Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/04e949b14d9b42538c8134e84850e944
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spelling	oai:doaj.org-article:04e949b14d9b42538c8134e84850e9442021-12-02T16:06:24ZReconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality10.1038/s41598-017-02762-52045-2322https://doaj.org/article/04e949b14d9b42538c8134e84850e9442017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02762-5https://doaj.org/toc/2045-2322Abstract Reconstruction of networks underlying complex systems is one of the most crucial problems in many areas of engineering and science. In this paper, rather than identifying parameters of complex systems governed by pre-defined models or taking some polynomial and rational functions as a prior information for subsequent model selection, we put forward a general framework for nonlinear causal network reconstruction from time-series with limited observations. With obtaining multi-source datasets based on the data-fusion strategy, we propose a novel method to handle nonlinearity and directionality of complex networked systems, namely group lasso nonlinear conditional granger causality. Specially, our method can exploit different sets of radial basis functions to approximate the nonlinear interactions between each pair of nodes and integrate sparsity into grouped variables selection. The performance characteristic of our approach is firstly assessed with two types of simulated datasets from nonlinear vector autoregressive model and nonlinear dynamic models, and then verified based on the benchmark datasets from DREAM3 Challenge4. Effects of data size and noise intensity are also discussed. All of the results demonstrate that the proposed method performs better in terms of higher area under precision-recall curve.Guanxue YangLin WangXiaofan WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-14 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guanxue Yang Lin Wang Xiaofan Wang Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality
description	Abstract Reconstruction of networks underlying complex systems is one of the most crucial problems in many areas of engineering and science. In this paper, rather than identifying parameters of complex systems governed by pre-defined models or taking some polynomial and rational functions as a prior information for subsequent model selection, we put forward a general framework for nonlinear causal network reconstruction from time-series with limited observations. With obtaining multi-source datasets based on the data-fusion strategy, we propose a novel method to handle nonlinearity and directionality of complex networked systems, namely group lasso nonlinear conditional granger causality. Specially, our method can exploit different sets of radial basis functions to approximate the nonlinear interactions between each pair of nodes and integrate sparsity into grouped variables selection. The performance characteristic of our approach is firstly assessed with two types of simulated datasets from nonlinear vector autoregressive model and nonlinear dynamic models, and then verified based on the benchmark datasets from DREAM3 Challenge4. Effects of data size and noise intensity are also discussed. All of the results demonstrate that the proposed method performs better in terms of higher area under precision-recall curve.
format	article
author	Guanxue Yang Lin Wang Xiaofan Wang
author_facet	Guanxue Yang Lin Wang Xiaofan Wang
author_sort	Guanxue Yang
title	Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality
title_short	Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality
title_full	Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality
title_fullStr	Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality
title_full_unstemmed	Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality
title_sort	reconstruction of complex directional networks with group lasso nonlinear conditional granger causality
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/04e949b14d9b42538c8134e84850e944
work_keys_str_mv	AT guanxueyang reconstructionofcomplexdirectionalnetworkswithgrouplassononlinearconditionalgrangercausality AT linwang reconstructionofcomplexdirectionalnetworkswithgrouplassononlinearconditionalgrangercausality AT xiaofanwang reconstructionofcomplexdirectionalnetworkswithgrouplassononlinearconditionalgrangercausality
_version_	1718385005057540096

Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality

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