  Topological properties of networks are widely applied to study the
link-prediction problem recently. Common Neighbors, for example, is a natural
yet efficient framework. Many variants of Common Neighbors have been thus
proposed to further boost the discriminative resolution of candidate links. In
this paper, we reexamine the role of network topology in predicting missing
links from the perspective of information theory, and present a practical
approach based on the mutual information of network structures. It not only can
improve the prediction accuracy substantially, but also experiences reasonable
computing complexity.
