Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Weighted Local Naive Bayes Link Prediction

  • Wu, JieHua (School of Computer Science and Engineering, Guangdong Polytechnic of Industry and Commerce) ;
  • Zhang, GuoJi (School of Computer Science and Engineering, South China University of Technology) ;
  • Ren, YaZhou (Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China) ;
  • Zhang, XiaYan (School of Computer Science and Engineering, South China University of Technology) ;
  • Yang, Qiao (School of Computer Science and Engineering, South China University of Technology)
  • Received : 2016.08.18
  • Accepted : 2017.03.04
  • Published : 2017.08.31
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Abstract

Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model-local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.

Keywords

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