Link Prediction in Social Networks: Role of Power Law Distribution

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.

Table of Contents

Chapter 1 Introduction
Chapter 2 Link Prediction Using Thresholding Nodes Based on Their Degree
Chapter 3 Locally Adaptive Link Prediction
Chapter 4 Two-Phase Framework for Link Prediction
Chapter 5 Applications of Link Prediction
Chapter 6 Conclusion