  Link prediction aims to predict the potential existence of links between two
unconnected nodes within a network based on the known topological
characteristics. Evaluation metrics are used to assess the effectiveness of
algorithms in link prediction. The discriminating ability of these evaluation
metrics is vitally important for accurately evaluating link prediction
algorithms. In this study, we propose an artificial network model, based on
which one can adjust a single parameter to monotonically and continuously turn
the prediction accuracy of the specifically designed link prediction algorithm.
Building upon this foundation, we show a framework to depict the effectiveness
of evaluating metrics by focusing on their discriminating ability.
Specifically, a quantitative comparison in the abilities of correctly
discerning varying prediction accuracies was conducted encompassing nine
evaluation metrics: Precision, Recall, F1-Measure, Matthews Correlation
Coefficient (MCC), Balanced Precision (BP), the Area Under the receiver
operating characteristic Curve (AUC), the Area Under the Precision-Recall curve
(AUPR), Normalized Discounted Cumulative Gain (NDCG), and the Area Under the
magnified ROC (AUC-mROC). The results indicate that the discriminating
abilities of the three metrics, AUC, AUPR, and NDCG, are significantly higher
than those of other metrics.
