Learning Scale-Free Networks by Dynamic Node Specific Degree Prior

@inproceedings{Tang2015LearningSN,
  title={Learning Scale-Free Networks by Dynamic Node Specific Degree Prior},
  author={Qingming Tang and Siqi Sun and Jinbo Xu},
  booktitle={ICML},
  year={2015}
}
Learning network structure underlying data is an important problem in machine learning. This paper presents a novel degree prior to study the inference of scale-free networks, which are widely used to model social and biological networks. In particular, this paper formulates scale-free network inference using Gaussian Graphical model (GGM) regularized by a node degree prior. Our degree prior not only promotes a desirable global degree distribution, but also exploits the estimated degree of an… CONTINUE READING

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