Link prediction methods and their accuracy for different social networks and network metrics

Fei Gao, Katarzyna Musial*, Colin Cooper, Sophia Tsoka

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)


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 networkmetrics and prediction accuracy of prediction methodsmay formthe basis of ametalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.
Original languageEnglish
Article number172879
JournalScientific Programming
Publication statusPublished - 2015


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