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Scientific Programming
Volume 2015 (2015), Article ID 172879, 13 pages
http://dx.doi.org/10.1155/2015/172879
Research Article

Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, Strand Campus, London WC2R 2LS, UK

Received 27 February 2014; Revised 21 November 2014; Accepted 21 November 2014

Academic Editor: Jeffrey C. Carver

Copyright © 2015 Fei Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.