  As a fundamental challenge in vast disciplines, link prediction aims to
identify potential links in a network based on the incomplete observed
information, which has broad applications ranging from uncovering missing
protein-protein interaction to predicting the evolution of networks. One of the
most influential methods rely on similarity indices characterized by the common
neighbors or its variations. We construct a hidden space mapping a network into
Euclidean space based solely on the connection structures of a network.
Compared with real geographical locations of nodes, our reconstructed locations
are in conformity with those real ones. The distances between nodes in our
hidden space could serve as a novel similarity metric in link prediction. In
addition, we hybrid our hidden space method with other state-of-the-art
similarity methods which substantially outperforms the existing methods on the
prediction accuracy. Hence, our hidden space reconstruction model provides a
fresh perspective to understand the network structure, which in particular
casts a new light on link prediction.
