  Common-neighbor-based method is simple yet effective to predict missing
links, which assume that two nodes are more likely to be connected if they have
more common neighbors. In such method, each common neighbor of two nodes
contributes equally to the connection likelihood. In this Letter, we argue that
different common neighbors may play different roles and thus lead to different
contributions, and propose a local na\"{\i}ve Bayes model accordingly.
Extensive experiments were carried out on eight real networks. Compared with
the common-neighbor-based methods, the present method can provide more accurate
predictions. Finally, we gave a detailed case study on the US air
transportation network.
