Name: Social Networks With Rich Edge Semantics (Chapman & Hall (en Inglés)
Price: 297.09 PEN
Availability: InStock
Author: Quan Zheng
ISBN: 9780367573256

portada Social Networks With Rich Edge Semantics (Chapman & Hall (en Inglés)

Social Networks With Rich Edge Semantics (Chapman & Hall (en Inglés)

Formato

Libro Físico

Editorial

Crc Press

Autor

Quan Zheng

Año

2020

Idioma

Inglés

N° páginas

230

Encuadernación

Tapa Blanda

ISBN13

9780367573256

N° edición

Categorías

Econometría

Social Networks With Rich Edge Semantics (Chapman & Hall (en Inglés)

Quan Zheng (Autor) · Crc Press · Tapa Blanda

Más barato Libro Nuevo Importado

Envío: 17 a 22 días háb.

S/ 660,21S/ 297,09

-55%

Más rápido Libro Nuevo Importado

Envío: 13 a 18 días háb.

S/ 677,51S/ 304,88

-55%

Costos de importación incluídos en el precio ✅

Libro Nuevo Más barato

Quedan 100 unidades

S/ 297,09

Llega entre el 10 Jul y el 20 Jul a CERCADO DE LIMA, Lima. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Social Networks With Rich Edge Semantics (Chapman & Hall (en Inglés)"

Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets.FeaturesIntroduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over timePresents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzedIncludes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriateShows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a nodeIllustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groupsSuitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks.