Link Prediction in Social Networks - Role of Power Law Distribution (Paperback, 1st ed. 2016)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Computer Science
Release date:	2016
First published:	2016
Authors:	Srinivas Virinchi • Pabitra Mitra
Dimensions:	235 x 155 x 4mm (L x W x T)
Format:	Paperback
Pages:	67
Edition:	1st ed. 2016
ISBN-13:	978-3-319-28921-2
Categories:	Books > Computing & IT > General theory of computing > Systems analysis & design Books > Computing & IT > Internet > Network computers Books > Computing & IT > Applications of computing > Databases > Data mining Promotions
LSN:	3-319-28921-7
Barcode:	9783319289212

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