Name: The Shortest Path to Network Geometry: A Practical Guide to Basic Models and Applications (Elements in Structure and Dynamics of Complex Networks) (en Inglés)
Price: 30910.00 CLP
Availability: InStock
Author: Serrano, M. Ángeles ; Boguñá, Marián
ISBN: 9781108791083

portada The Shortest Path to Network Geometry: A Practical Guide to Basic Models and Applications (Elements in Structure and Dynamics of Complex Networks) (en Inglés)

Formato

Libro Físico

Autor

M. Ángeles Serrano

Marián Boguñá

Editorial

Cambridge University Press

Año

2021

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22.9 x 15.2 x 0.3 cm

Peso

0.09 kg.

ISBN13

9781108791083

Categorías

Física matemática

The Shortest Path to Network Geometry: A Practical Guide to Basic Models and Applications (Elements in Structure and Dynamics of Complex Networks) (en Inglés)

M. Ángeles Serrano (Autor) · Marián Boguñá (Autor) · Cambridge University Press · Tapa Blanda

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Reseña del libro "The Shortest Path to Network Geometry: A Practical Guide to Basic Models and Applications (Elements in Structure and Dynamics of Complex Networks) (en Inglés)"

Real networks comprise from hundreds to millions of interacting elements and permeate all contexts, from technology to biology to society. All of them display non-trivial connectivity patterns, including the small-world phenomenon, making nodes to be separated by a small number of intermediate links. As a consequence, networks present an apparent lack of metric structure and are difficult to map. Yet, many networks have a hidden geometry that enables meaningful maps in the two-dimensional hyperbolic plane. The discovery of such hidden geometry and the understanding of its role have become fundamental questions in network science giving rise to the field of network geometry. This Element reviews fundamental models and methods for the geometric description of real networks with a focus on applications of real network maps, including decentralized routing protocols, geometric community detection, and the self-similar multiscale unfolding of networks by geometric renormalization.