Predicting missing links via correlation between nodes

Abstract

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

7 pages, 3 figures, 2 tables. arXiv admin note: text overlap with arXiv:1010.0725 by other authors

References in corpus (7)

• Finding community structure in networks using the eigenvectors of matrices
• Hierarchical structure and the prediction of missing links in networks
• Stochastic blockmodels and community structure in networks
• Predicting Missing Links via Local Information
• Effective and Efficient Similarity Index for Link Prediction of Complex Networks
• Identifying the role that individual animals play in their social network
• Multi-Scale Link Prediction