The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Prediction and ranking algorithms for event-based network data
ACM SIGKDD Explorations Newsletter
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Link Prediction of Social Networks Based on Weighted Proximity Measures
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
New perspectives and methods in link prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting place features in link prediction on location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised link prediction using aggregative statistics on heterogeneous social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Link prediction with social vector clocks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Drug-target interaction prediction for drug repurposing with probabilistic similarity logic
Proceedings of the 12th International Workshop on Data Mining in Bioinformatics
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Link prediction is a popular area for publication. Papers appear in virtually every conference on data mining or network science with new methods. We argue that the practical performance potential of these methods is generally unknown because of challenges endemic to evaluation in many link prediction contexts. We demonstrate that current methods of evaluation are inadequate and can lead to woefully errant conclusions about practical performance potential. We argue for the use of precision-recall threshold curves and associated areas in lieu of receiver operating characteristic curves due to the extreme imbalance of the link prediction classification problem. We provide empirical examples of how current methods lead to questionable conclusions, how the fallacy of these conclusions is illuminated by methods we propose, and suggest a fair and consistent framework for link prediction evaluation for longitudinal and non-longitudinal network data sets.