株式会社極東書店トップ ＞ 商品一覧 ＞ Network Algorithms, Data Mining, and Applications : NET, Moscow, Russia, May 2018. 1st ed. 2020.
商品詳細
Network Algorithms, Data Mining, and Applications : NET, Moscow, Russia, May 2018. 1st ed. 2020.
・ISBN 978-3-030-37159-3 paper EUR 99.99
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| 著者・編者 | Bychkov, Ilya / Kalyagin, Valery A. / Pardalos, Panos M. / Prokopyev, Oleg (eds.), |
|---|---|
| シリーズ | Springer Proceedings in Mathematics & Statistics |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2021 |
| ページ数 | 244 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-5493＞ |
解説
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.