SpringerBriefs in Computer Science

Link Prediction in Social Networks

Role of Power Law Distribution

Authors: Srinivas, Virinchi, Mitra, Pabitra

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  • Presents an accessible explanation of the role of power law degree distribution in link prediction
  • Describes a range of link prediction algorithms in an easy-to-understand manner
  • Discusses the implementation of both the popular link prediction algorithms and the proposed link prediction algorithms in C++
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eBook 41,64 €
price for Spain (gross)
  • ISBN 978-3-319-28922-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 51,99 €
price for Spain (gross)
  • ISBN 978-3-319-28921-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

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.

About the authors

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.

Table of contents (6 chapters)

  • Introduction

    Srinivas, Virinchi (et al.)

    Pages 1-14

    Preview Buy Chapter 30,19 €
  • Link Prediction Using Thresholding Nodes Based on Their Degree

    Srinivas, Virinchi (et al.)

    Pages 15-25

    Preview Buy Chapter 30,19 €
  • Locally Adaptive Link Prediction

    Srinivas, Virinchi (et al.)

    Pages 27-44

    Preview Buy Chapter 30,19 €
  • Two-Phase Framework for Link Prediction

    Srinivas, Virinchi (et al.)

    Pages 45-55

    Preview Buy Chapter 30,19 €
  • Applications of Link Prediction

    Srinivas, Virinchi (et al.)

    Pages 57-61

    Preview Buy Chapter 30,19 €

Buy this book

eBook 41,64 €
price for Spain (gross)
  • ISBN 978-3-319-28922-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 51,99 €
price for Spain (gross)
  • ISBN 978-3-319-28921-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Link Prediction in Social Networks
Book Subtitle
Role of Power Law Distribution
Authors
Series Title
SpringerBriefs in Computer Science
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
The Author(s)
eBook ISBN
978-3-319-28922-9
DOI
10.1007/978-3-319-28922-9
Softcover ISBN
978-3-319-28921-2
Series ISSN
2191-5768
Edition Number
1
Number of Pages
IX, 67
Number of Illustrations
5 illustrations in colour
Topics