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Link prediction in social networksro...

Mitra, Pabitra.

Link prediction in social networksrole of power law distribution /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Link prediction in social networksby Virinchi Srinivas, Pabitra Mitra.
Reminder of title:	role of power law distribution /
Author:	Srinivas, Virinchi.
other author:	Mitra, Pabitra.
Published:	Cham :Springer International Publishing :2016.
Description:	ix, 67 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Data mining.
Online resource:	http://dx.doi.org/10.1007/978-3-319-28922-9
ISBN:	9783319289229$q(electronic bk.)

Link prediction in social networksrole of power law distribution /
Srinivas, Virinchi.

Link prediction in social networksrole of power law distribution /[electronic resource] :by Virinchi Srinivas, Pabitra Mitra. - Cham :Springer International Publishing :2016. - ix, 67 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Introduction -- Link Prediction Using Degree Thresholding -- Locally Adaptive Link Prediction -- Two Phase Framework for Link Prediction -- Applications of Link Prediction -- Conclusion.

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.

ISBN: 9783319289229$q(electronic bk.)

Standard No.: 10.1007/978-3-319-28922-9doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Link prediction in social networksrole of power law distribution /
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505 0 $a Introduction -- Link Prediction Using Degree Thresholding -- Locally Adaptive Link Prediction -- Two Phase Framework for Link Prediction -- Applications of Link Prediction -- Conclusion.
520 $a 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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