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Titlebook: Computational Aspects and Applications in Large-Scale Networks; NET 2017, Nizhny Nov Valery A. Kalyagin,Panos M. Pardalos,Irina Utkina Conf

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書目名稱Computational Aspects and Applications in Large-Scale Networks
副標(biāo)題NET 2017, Nizhny Nov
編輯Valery A. Kalyagin,Panos M. Pardalos,Irina Utkina
視頻videohttp://file.papertrans.cn/233/232113/232113.mp4
概述Presents state-of-the-art techniques in modern network analysis for large-scale networks.Features new theoretical models, approaches, and tools for network analysis.Broadens understanding of computati
叢書名稱Springer Proceedings in Mathematics & Statistics
圖書封面Titlebook: Computational Aspects and Applications in Large-Scale Networks; NET 2017, Nizhny Nov Valery A. Kalyagin,Panos M. Pardalos,Irina Utkina Conf
描述.Contributions in this volume focus on computationally efficient algorithms and rigorous mathematical theories for analyzing large-scale networks. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks..This proceeding is a result of the 7th International Conference in Network Analysis, held at the Higher School of Economics, Nizhny Novgorod in June 2017. The conference brought together scientists, engineers, and researchers from academia, industry, and government.
出版日期Conference proceedings 2018
關(guān)鍵詞Computational Aspects and Applications of Large Scale Networks; Critical Links; Autoregressive Models;
版次1
doihttps://doi.org/10.1007/978-3-319-96247-4
isbn_softcover978-3-030-07163-9
isbn_ebook978-3-319-96247-4Series ISSN 2194-1009 Series E-ISSN 2194-1017
issn_series 2194-1009
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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Robust Statistical Procedures for Testing Dynamics in Market Networkmarket network. In the present paper, the stock market network of stock’s returns is considered. Probability of sign coincidence of stock’s returns is used as the measure of similarity between stocks. Robust (distribution free) multiple testing statistical procedure for testing dynamics of network i
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