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As a fundamental problem in many different fields, link prediction aims toestimate the likelihood of an existing link between two nodes based on theobserved information. Since this problem is related to many applicationsranging from uncovering missing data to predicting the evolution of networks,link prediction has been intensively investigated recently and many methodshave been proposed so far. The essential challenge of link prediction is toestimate the similarity between nodes. Most of the existing methods are basedon the common neighbor index and its variants. In this paper, we propose tocalculate the similarity between nodes by the correlation coefficient. Thismethod is found to be very effective when applied to calculate similarity basedon high order paths. We finally fuse the correlation-based method with theresource allocation method, and find that the combined method can substantiallyoutperform the existing methods, especially in sparse networks.

Predicting missing links via correlation between nodes