Statistical Analysis of Graph Structures in Random Variable Networks - Brossura

Libro 39 di 43: SpringerBriefs in Optimization

Kalyagin, V. A.; Koldanov, A. P.; Koldanov, P. A.; Pardalos, P. M.

 
9783030602925: Statistical Analysis of Graph Structures in Random Variable Networks
Brossura
ISBN 10: 3030602923 ISBN 13: 9783030602925
Casa editrice: Springer, 2020

Sinossi

This book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable network is introduced, uncertainty of identification procedure is defined through a risk function, random variables networks with different measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also introduces a new class of identification algorithms based on a new measure of similarity and prove its robustness in a large class of distributions, and presents applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks through a theoretical analysis that identifies network structures. Both researchers and graduate students in computer science, mathematics, and optimization will find the applications and techniques presented useful.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer
Data di pubblicazione
2020
Lingua
Inglese
ISBN 10 
3030602923
ISBN 13 
9783030602925
Rilegatura
Copertina flessibile
Numero edizione
1
Numero di pagine
112
Contatto del produttore
ProductSafety@springernature.com
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