Link Prediction in Social Networks

Role of Power Law Distribution

Nonfiction, Computers, Networking & Communications, Hardware, Database Management, General Computing
Cover of the book Link Prediction in Social Networks by Pabitra Mitra, Srinivas Virinchi, Springer International Publishing
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Author: Pabitra Mitra, Srinivas Virinchi ISBN: 9783319289229
Publisher: Springer International Publishing Publication: January 22, 2016
Imprint: Springer Language: English
Author: Pabitra Mitra, Srinivas Virinchi
ISBN: 9783319289229
Publisher: Springer International Publishing
Publication: January 22, 2016
Imprint: Springer
Language: English

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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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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