Band 315
Network Algorithms, Data Mining, and Applications NET, Moscow, Russia, May 2018
Aus der Reihe
Springer Proceedings in Mathematics & Statistics
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- Hardcover
- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
Fr. 137.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
23.02.2021
Abbildungen
XIII, 244 p. 65 illus., 43 illus. in color.
Herausgeber
Ilya Bychkov + weitereVerlag
SpringerSeitenzahl
244
Maße (L/B/H)
23.5/15.5/1.5 cm
Gewicht
400 g
Auflage
1st ed. 2020
Sprache
Englisch
ISBN
978-3-030-37159-3
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.
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.
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