Link Prediction in Social Networks

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.

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

Autorentext

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.

Inhalt

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