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arXiv · 1705.02199

Hidden space reconstruction inspires link prediction in complex networks

Hao Liao·Mingyang Zhou·Zong-Wen Wei·Rui Mao·Alexandre Vidmer·Yi-Cheng Zhang
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Abstract

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.

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Hao Liao, Mingyang Zhou, Zong-Wen Wei, Rui Mao, Alexandre Vidmer, Yi-Cheng Zhang. 2017-03-25. Hidden space reconstruction inspires link prediction in complex networks. https://arxiv.org/abs/1705.02199

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