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Statistical Analysis of Graph Structures in Random Variable Networks

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 netwo...

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Bibliographic Details
Main Authors: Kalyagin, V. A., Koldanov, A. P. (Author), Koldanov, P. A. (Author), Pardalos, P. M. (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2020, 2020
Edition:1st ed. 2020
Series:SpringerBriefs in Optimization
Subjects:
Optimization
Mathematical Models Of Cognitive Processes And Neural Networks
Probability Theory
Computer Networks 
Computer Engineering And Networks
Neural Networks (computer Science) 
Computer Engineering
Mathematical Optimization
Probabilities
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Description
Summary: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
Physical Description:VIII, 101 p. 9 illus., 3 illus. in color online resource
ISBN:9783030602932

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