Link Prediction in Social Networks: Role of Power Law Distribution - Brossura

Virinchi, Srinivas

Brossura

ISBN 10: 3319289217 ISBN 13: 9783319289212

Casa editrice: Springer, 2016

Riassunto

Thiswork presents link prediction similarity measures for social networks that exploitthe degree distribution of the networks. In the context of link prediction indense networks, the text proposes similarity measures based on Markov inequalitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Also presented are similarity measures based on cliques(CNC, AAC, RAC), which assign extra weight between nodes sharing a greater numberof cliques. Additionally, a locally adaptive (LA) similarity measure isproposed that assigns different weights to common nodes based on the degreedistribution of the local neighborhood and the degree distribution of thenetwork. In the context of link prediction in dense networks, the textintroduces a novel two-phase framework that adds edges to the sparse graph toforma boost graph.

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Informazioni sull?autore

Dr. Virinchi Srinivas is a Graduate Research Assistant inthe Department of Computer Science at the University of Maryland, College Park,MD, USA.

Dr. Pabitra Mitra is an Associate Professor in the Departmentof Computer Science and Engineering at the Indian Institute of Technology,Kharagpur, India.

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3319289217
• ISBN 13: 9783319289212
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 80
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania