Implementation of Stochastic Block Model with several inference strategy (VBEM, VEM, Natural conjugate gradients)
Project description
SBM package
Installation
To install this package, run
python -m pip install sbm-vbem
Quick use of the SBM function
In this package, I implemented 2 different estimations : a variational-bayes EM (it's on the way) and a variational-EM. The variational-bayes EM can be optimized in two ways : setting the lagrangian to zero, referred to as the VBEM algorithm, or using a natural-conjugate gradient method, referred to as the NCG algorithm.
The main function sbm is only implemented for directed graphs (for now) and can be used this way:
- with no init given :
sbm(adj, algo='vbem', init='kmeans') - with an already computed init :
sbm(adj, algo='vbem', tau_init=tau)
This is a joint work with Pr. Pierre Latouche and Pr. Charles Bouveyron.
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