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Thiswork presents link prediction similarity measures for social networks that exploitthe degree distribution of the networks. In the context of link prediction indense networks, the text proposes similarity measures based on Markov inequalitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Also presented are similarity measures based on cliques(CNC, AAC, RAC), which assign extra weight between nodes sharing a greater numberof cliques. Additionally, a locally adaptive (LA) similarity measure isproposed that assigns different weights to common nodes based on the degreedistribution of the local neighborhood and the degree distribution of thenetwork. In the context of link prediction in dense networks, the textintroduces a novel two-phase framework that adds edges to the sparse graph toforma boost graph.
List of contents
Introduction.- Link Prediction Using Degree Thresholding.- Locally Adaptive Link Prediction.- Two Phase Framework for Link Prediction.- Applications of Link Prediction.- Conclusion.
About the author
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.
Summary
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.
