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Link Prediction in Social Networks

Role of Power Law Distribution

  • Virinchi Srinivas
  • Pabitra Mitra

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Virinchi Srinivas, Pabitra Mitra
    Pages 1-14
  3. Virinchi Srinivas, Pabitra Mitra
    Pages 15-25
  4. Virinchi Srinivas, Pabitra Mitra
    Pages 27-44
  5. Virinchi Srinivas, Pabitra Mitra
    Pages 45-55
  6. Virinchi Srinivas, Pabitra Mitra
    Pages 57-61
  7. Virinchi Srinivas, Pabitra Mitra
    Pages 63-64
  8. Back Matter
    Pages 65-67

About this book

Introduction

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.

Keywords

Link Prediction Power Law Degree Distribution Local Neighborhood Recommender Systems Graph Mining

Authors and affiliations

  • Virinchi Srinivas
    • 1
  • Pabitra Mitra
    • 2
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA
  2. 2.Dept. Computer Sci & Engg,R No: CS310Indian Institute of Technology KharagpurKharagpurIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-28922-9
  • Copyright Information The Author(s) 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-28921-2
  • Online ISBN 978-3-319-28922-9
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site
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