Link Prediction in Social Networks Role of Power Law Distribution
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Link Prediction in Social Networks Role of Power Law Distribution

SKU: 9783319289229

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Additional information

Full Title

Link Prediction in Social Networks Role of Power Law Distribution
Author(s)

Srinivas Virinchi, Pabitra Mitra
Edition
ISBN

9783319289229, 9783319289212
Publisher

Springer
Format

PDF and EPUB

Description

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.

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