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© 2016

Link Prediction in Social Networks

Role of Power Law Distribution

Benefits

  • accessible explanation of the role of power law degree distribution in link

  • 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++

Book

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

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

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.

Bibliographic information

  • Book Title Link Prediction in Social Networks
  • Book Subtitle Role of Power Law Distribution
  • Authors Srinivas Virinchi
    Pabitra Mitra
  • Series Title SpringerBriefs in Computer Science
  • Series Abbreviated Title SpringerBriefs Computer Sci.
  • 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 Computer Science (R0)
  • Softcover ISBN 978-3-319-28921-2
  • eBook ISBN 978-3-319-28922-9
  • Series ISSN 2191-5768
  • Series E-ISSN 2191-5776
  • Edition Number 1
  • Number of Pages IX, 67
  • Number of Illustrations 0 b/w illustrations, 5 illustrations in colour
  • Topics Data Mining and Knowledge Discovery
    Computer Communication Networks
  • Buy this book on publisher's site
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