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＜E-Book＞
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)

Edition	1st ed. 2020.
Publisher	(Cham : Springer International Publishing : Imprint: Springer)
Year	2020
Language	English
Size	VIII, 101 p. 9 illus., 3 illus. in color : online resource
Authors	*Kalyagin, V. A author Koldanov, A. P author Koldanov, P. A author Pardalos, P. M author SpringerLink (Online service)
Subjects	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
Notes	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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