Overview
- 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++
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents(6 chapters)
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.
Authors and Affiliations
-
Department of Computer Science, University of Maryland, College Park, USA
Virinchi Srinivas
-
Dept. Computer Sci & Engg,R No: CS310, Indian Institute of Technology Kharagpur, Kharagpur, India
Pabitra Mitra
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: Virinchi Srinivas, Pabitra Mitra
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-28922-9
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2016
Softcover ISBN: 978-3-319-28921-2Published: 29 January 2016
eBook ISBN: 978-3-319-28922-9Published: 22 January 2016
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: IX, 67
Number of Illustrations: 5 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Computer Communication Networks