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Predicting missing links via correlation between nodes

Author

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  • Liao, Hao
  • Zeng, An
  • Zhang, Yi-Cheng

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 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.

Suggested Citation

  • Liao, Hao & Zeng, An & Zhang, Yi-Cheng, 2015. "Predicting missing links via correlation between nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 216-223.
    • Handle: RePEc:eee:phsmap:v:436:y:2015:i:c:p:216-223
    DOI: 10.1016/j.physa.2015.05.009
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    References listed on IDEAS

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    Cited by:

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    7. Thorben Funke & Till Becker, 2019. "Stochastic block models: A comparison of variants and inference methods," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-40, April.

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