Link Prediction in Social Networks

Role of Power Law Distribution
1. Auflage 2016
ISBN: 978-3-319-28921-2
Verlag: Springer International Publishing

Buch, Englisch, 67 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1358 g

Reihe: SpringerBriefs in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Mitra, Pabitra

Virinchi, Srinivas

Fachgebiete

• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Social Media, Semantic Web, Web 2.0
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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

Über Autor(innen)

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.