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An implementation of bayesian cut methods

Project description

The Bayesian Cut Python package provides an easy to use API for the straight-forward application of Bayesian network cuts using a full Bayesian inference framework based on the Gibbs-Sampler using the degree corrected Stochastic Blockmodel (dc-SBM) or the Bayesian Cut (BC). Furthermore it provides modularity, ratio-cut and norm cut based spectral network cut methods. It also provides a rich visualization library that allow an easy analysis of posterior solution landscapes and network cuts obtained by the various methods.

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