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Link prediction is a technique that uses the topological information in agiven network to infer the missing links in it. Since past research on linkprediction has primarily focused on enhancing performance for given empiricalsystems, negligible attention has been devoted to link prediction with regardto network models. In this paper, we thus apply link prediction to two networkmodels: The Watts-Strogatz (WS) model and Barab\'asi-Albert (BA) model. Weattempt to gain a better understanding of the relation between accuracy andeach network parameter (mean degree, the number of nodes and the rewiringprobability in the WS model) through network models. Six similarity indices areused, with precision and area under the ROC curve (AUC) value as the accuracymetrics. We observe a positive correlation between mean degree and accuracy,and size independence of the AUC value.

Accuracy Test for Link Prediction in terms of Similarity Index: The Case of WS and BA Models