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Titlebook: Network Algorithms, Data Mining, and Applications; NET, Moscow, Russia, Ilya Bychkov,Valery A. Kalyagin,Oleg Prokopyev Conference proceedin

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書(shū)目名稱(chēng)Network Algorithms, Data Mining, and Applications
副標(biāo)題NET, Moscow, Russia,
編輯Ilya Bychkov,Valery A. Kalyagin,Oleg Prokopyev
視頻videohttp://file.papertrans.cn/663/662762/662762.mp4
概述Introduces state-of-the-art techniques in computer science and network analysis.Features new theoretical models and approaches for network analysis with new efficient tools.Presents a range of applica
叢書(shū)名稱(chēng)Springer Proceedings in Mathematics & Statistics
圖書(shū)封面Titlebook: Network Algorithms, Data Mining, and Applications; NET, Moscow, Russia, Ilya Bychkov,Valery A. Kalyagin,Oleg Prokopyev Conference proceedin
描述This proceedings presents the result of the 8th International Conference in Network Analysis, held at the Higher School of Economics, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government.?.Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, computer science, and engineering 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. Machine learning techniques in network settings including community detection, clustering, andbiclustering algorithms are presented with applications to social network analysis..
出版日期Conference proceedings 2020
關(guān)鍵詞Network algorithms; Clusters; Information Theory; graph dissimilarities; Metaheuristics; Large-Scale Grap
版次1
doihttps://doi.org/10.1007/978-3-030-37157-9
isbn_softcover978-3-030-37159-3
isbn_ebook978-3-030-37157-9Series ISSN 2194-1009 Series E-ISSN 2194-1017
issn_series 2194-1009
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Hamoud S. Bin-Obaid,Theodore B. Trafalisined network traffic. However, it is difficult to capture and estimate the volume of network traffic due to its time-varying nature. In this paper, we study the network traffic estimation scheme to estimate the fine-grained network traffic. Firstly, the network traffic is constructed as a time serie
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Dmitry I. Ignatov,Polina Ivanova,Albina Zamaletdinovanting fast simulation frameworks to estimate runtimes always results in tremendous slow-downs. In this paper, a quantization is done regarding the minimal overhead that can be expected when adding architectural models to a fast JIT enhanced emulation. Previous work is only focused on new approaches
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Oxana Mikhailova,Galina Gradoselskaya,Alexander Kharlamovior and improvements in teamwork performance; however, it is necessary to continue researching their impact on the organization and patient safety (Levine et al (eds), The comprehensive textbook of healthcare simulation. Springer, New York, 2013; Salas et al, J Hum Factors Ergonom Soc 48(2): 392–412
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Tamara Shcheglova,Galina Gradoselskaya,Ilia Karpovngs lag behind in the literature, even more when comes the necessity to implement them in mass. So, the problem is the following: in a context of simulation-based training, how might we conduct debriefings which enable effective reflection for learners and how to do this on a large scale? This chapt
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Dmitry Zaytsev,Gregory Khvatsky,Nikita Talovsky,Valentina Kuskovas between how learning is conceptualized and how simulation-based training programs are implemented. For research in education, learning theories circumscribe the phenomena that are examined. They have a substantial impact on methods, data analysis and interpretations of the results. We believe that
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