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Link prediction based on sampling in complex networks

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Abstract

The link prediction problem has received extensive attention in fields such as sociology, anthropology, information science, and computer science. In many practical applications, we only need to predict the potential links between the vertices of interest, instead of predicting all of the links in a complex network. In this paper, we propose a fast similarity based approach for predicting the links related to a given node. We construct a path set connected to the given node by a random walk. The similarity score is computed within a small sub-graph formed by the path set connected to the given node, which significantly reduces the computation time. By choosing the appropriate number of sampled paths, we can restrict the error of the estimated similarities within a given threshold. Our experimental results on a number of real networks indicate that the algorithm proposed in this paper can obtain accurate results in less time than existing methods.

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Authors and Affiliations

  1. Institute of Information Science and Technology, Yangzhou University, Yangzhou, China

    Caiyan Dai, Ling Chen & Bin Li

Authors
  1. Caiyan Dai
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  2. Ling Chen
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  3. Bin Li
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Corresponding author

Correspondence to Ling Chen.

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Conflict of interests

The authors declare no conflicts of interest.

Funding

This research was supported in part by the Chinese National Natural Science Foundation under grant Nos. 61379066, 61070047, 61379064, 61472344, and 61402395;theNatural Science Foundation of Jiangsu Province under contracts BK20130452, BK2012672, BK2012128, and BK20140492;the Natural Science Foundation of the Education Department of Jiangsu Province under contracts 12KJB520019, 13KJB520026, and 09KJB20013;and the Six Talent Peaks Project in Jiangsu Province(Grant No. 2011-DZXX-032).

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Dai, C., Chen, L. & Li, B. Link prediction based on sampling in complex networks. Appl Intell 47, 1–12 (2017). https://doi.org/10.1007/s10489-016-0872-1

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  • DOI: https://doi.org/10.1007/s10489-016-0872-1

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