IEEE Access (Jan 2019)

Predicting Top-<inline-formula> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula> Missing Links: An Improved Local Na&#x00EF;ve Bayes Model

  • Longjie Li,
  • Shijin Xu,
  • Mingwei Leng,
  • Shiyu Fang,
  • Xiaoyun Chen

DOI
https://doi.org/10.1109/ACCESS.2019.2914724
Journal volume & issue
Vol. 7
pp. 57868 – 57880

Abstract

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The problem of link prediction has captured considerable attention from various disciplines due to its wide range of applications. A multitude of link prediction methods have been proposed with various techniques. The local Naïve Bayes (LNB) model is an effective one, which discriminates the contribution of different common neighbors by a role function. This paper proposes a new link prediction method, which further enhances the accuracy of the LNB model by considering the local community links and the degree of seed nodes. The experimental results on 12 real-world networks demonstrate that the proposed method outperforms the compared methods in the top-L link prediction task.

Keywords