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As a fundamental challenge in vast disciplines, link prediction aims toidentify potential links in a network based on the incomplete observedinformation, which has broad applications ranging from uncovering missingprotein-protein interaction to predicting the evolution of networks. One of themost influential methods rely on similarity indices characterized by the commonneighbors or its variations. We construct a hidden space mapping a network intoEuclidean space based solely on the connection structures of a network.Compared with real geographical locations of nodes, our reconstructed locationsare in conformity with those real ones. The distances between nodes in ourhidden space could serve as a novel similarity metric in link prediction. Inaddition, we hybrid our hidden space method with other state-of-the-artsimilarity methods which substantially outperforms the existing methods on theprediction accuracy. Hence, our hidden space reconstruction model provides afresh perspective to understand the network structure, which in particularcasts a new light on link prediction.

Hidden space reconstruction inspires link prediction in complex networks