Corpus ID: 53580196

Nonparametric Bayesian models of hierarchical structure in complex networks

@article{Schmidt2013NonparametricBM,
  title={Nonparametric Bayesian models of hierarchical structure in complex networks},
  author={M. Schmidt and Tue Herlau and M. M{\o}rup},
  journal={arXiv: Machine Learning},
  year={2013}
}
  • M. Schmidt, Tue Herlau, M. Mørup
  • Published 2013
  • Computer Science, Mathematics
  • arXiv: Machine Learning
  • Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can have prominent patterns on different scales, calling for a hierarchically structured model. We propose two non-parametric Bayesian hierarchical network models based on Gibbs fragmentation tree priors, and demonstrate their ability to capture nested patterns in simulated networks. On real networks we demonstrate… CONTINUE READING

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