Network Algorithms, Data Mining, and Applications: NET, Moscow, Russia, May 2018 (Springer Proceedings in Mathematics & Statistics)

ISBN 10: 303037159X / ISBN 13: 9783030371593

Verlag: Springer, 2021

Beschreibung

Bestandsnummer des Verkäufers ABLIING23Mar3113020015323

Bibliografische Details

• Titel: Network Algorithms, Data Mining, and Applications: NET, Moscow, Russia, May 2018 (Springer Proceedings in Mathematics & Statistics)
• Verlag: Springer
• Erscheinungsjahr: 2021
• Sprache: Englisch
• ISBN-10: 303037159X
• ISBN-13: 9783030371593
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

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, and biclustering algorithms are presented with applications to social network analysis.

Von der hinteren Coverseite

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, and biclustering algorithms are presented with applications to social network analysis.