Statistical Link Label Modeling for Sign Prediction: Smoothing Sparsity by Joining Local and Global Information

@article{Javari2017StatisticalLL,
  title={Statistical Link Label Modeling for Sign Prediction: Smoothing Sparsity by Joining Local and Global Information},
  author={Amin Javari and Hongxiang Qiu and Elham Barzegaran and Mahdi Jalili and Kevin Chen-Chuan Chang},
  journal={2017 IEEE International Conference on Data Mining (ICDM)},
  year={2017},
  pages={1039-1044}
}
One of the major issues in signed networks is to use network structure to predict the missing sign of an edge. In this paper, we introduce a novel probabilistic approach for the sign prediction problem. The main characteristic of the proposed models is their ability to adapt to the sparsity level of an input network. Building a model that has an ability to adapt to the sparsity of the data has not yet been considered in the previous related works. We suggest that there exists a dilemma between… 

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