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＜電子ブック＞
Statistical Analysis of Graph Structures in Random Variable Networks / by V. A. Kalyagin, A. P. Koldanov, P. A. Koldanov, P. M. Pardalos
(SpringerBriefs in Optimization. ISSN:2191575X)

版	1st ed. 2020.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2020
本文言語	英語
大きさ	VIII, 101 p. 9 illus., 3 illus. in color : online resource
著者標目	*Kalyagin, V. A author Koldanov, A. P author Koldanov, P. A author Pardalos, P. M author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Computer engineering LCSH:Computer networks  LCSH:Probabilities LCSH:Neural networks (Computer science)  FREE:Optimization FREE:Computer Engineering and Networks FREE:Probability Theory FREE:Mathematical Models of Cognitive Processes and Neural Networks
一般注記	1. Introduction -- 2. Random variable networks. -3. Network Identification Structure Algorithms -- 4. Uncertainty of Network Structure Identification -- 5. Robustness of Network Structure Identification -- 6. Optimality of Network Structure Identification -- 7. Applications to Market Network Analysis -- 8. Conclusion -- 9. References This book presents new theoretical approaches for statistical network analysis in random variable networks. Robustness and optimality of statistical procedures for various network structures are detailed and analyzed. Applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks are presented through a theoretical analysis which identifies network structures. Graduate students and researchers in computer science, mathematics, and optimization will find the applications and techniques presented useful HTTP:URL=https://doi.org/10.1007/978-3-030-60293-2

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