Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 436, Issue -, Pages 216-223Publisher
ELSEVIER
DOI: 10.1016/j.physa.2015.05.009
Keywords
Link prediction; Correlation coefficient; Node similarity
Categories
Funding
- EU [611272]
- Swiss National Science Foundation [200020-143272]
- Opening Foundation of Alibaba Research Center for Complex Sciences, Hangzhou Normal University [PD12001003002008, PD12001003002006]
- National Science Foundation of China [U1301252, 61170076]
- Guangdong Natural Science Foundation [2014A030313553]
- Fundamental Research Funds for the Central Universities [2014ZM0079]
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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 Pearson 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. (C) 2015 Elsevier B.V. All rights reserved.
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