Link Prediction using Top-$k$ Shortest Distances
Social and Information Networks
2017-05-09 v1 Databases
Data Structures and Algorithms
Abstract
In this paper, we apply an efficient top- shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Our results show that using top- distances as a similarity measure outperforms classical similarity measures such as Jaccard and Adamic/Adar.
Keywords
Cite
@article{arxiv.1705.02936,
title = {Link Prediction using Top-$k$ Shortest Distances},
author = {Andrei Lebedev and JooYoung Lee and Victor Rivera and Manuel Mazzara},
journal= {arXiv preprint arXiv:1705.02936},
year = {2017}
}