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