Name: Link Prediction in Social Networks: Role of Power law Distribution (Springerbriefs in Computer Science) (en Inglés)
Price: 75.93 EUR
Availability: InStock
Author: Virinchi Srinivas; Pabitra Mitra
ISBN: 9783319289212

portada Link Prediction in Social Networks: Role of Power law Distribution (Springerbriefs in Computer Science) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Virinchi Srinivas; Pabitra Mitra

Año

2016

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9783319289212

N° edición

Categorías

Data mining: minería de datos

Link Prediction in Social Networks: Role of Power law Distribution (Springerbriefs in Computer Science) (en Inglés)

Virinchi Srinivas; Pabitra Mitra (Autor) · Springer · Tapa Blanda

Libro Nuevo

75,93 €

79,92 €

Ahorras: 4,00 €

5% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Viernes 17 de Mayo y el Jueves 06 de Junio.

Lo recibirás en cualquier lugar de España entre 1 y 5 días hábiles luego del envío.

Reseña del libro "Link Prediction in Social Networks: Role of Power law Distribution (Springerbriefs in Computer Science) (en Inglés)"

This work presents link prediction similarity measures for social networks that exploit the degree distribution of the networks. In the context of link prediction in dense networks, the text proposes similarity measures based on Markov inequality degree thresholding (MIDTs), which only consider nodes whose degree is above a threshold for a possible link. Also presented are similarity measures based on cliques (CNC, AAC, RAC), which assign extra weight between nodes sharing a greater number of cliques. Additionally, a locally adaptive (LA) similarity measure is proposed that assigns different weights to common nodes based on the degree distribution of the local neighborhood and the degree distribution of the network. In the context of link prediction in dense networks, the text introduces a novel two-phase framework that adds edges to the sparse graph to forma boost graph.