Virinchi Srinivas, Pabitra Mitra

Link Prediction in Social Networks

Role of Power Law Distribution

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• SpringerBriefs in Computer Science
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-28921-2
• 1st ed. 2016
• Seitenzahl: 80
• Erscheinungstermin: 29. Januar 2016
• Englisch
• Abmessung: 235mm x 155mm x 5mm
• Gewicht: 137g
• ISBN-13: 9783319289212
• ISBN-10: 3319289217
• Artikelnr.: 44247324

Autorenporträt

Dr. Virinchi Srinivas is a Graduate Research Assistant in the Department of Computer Science at the University of Maryland, College Park, MD, USA. Dr. Pabitra Mitra is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Kharagpur, India.

Inhaltsangabe

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

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