Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

SparseBM: A Python Module for Handling Sparse Graphs with Block Models

Abstract : The stochastic and latent block models are clustering and coclustering tools that are commonly used for network analyses, such as community detection or collaborative filtering. We present a variational inference algorithm for the stochastic block model and the latent block model for sparse graphs, which leverages on the sparsity of edges to scale up to a very large number of nodes. This algorithm is implemented in SparseBM, a Python module that takes advantage of the hardware speed up provided by graphics processing units (GPU).
Document type :
Preprints, Working Papers, ...
Complete list of metadata
Contributor : Gabriel Frisch Connect in order to contact the contributor
Submitted on : Friday, February 12, 2021 - 10:32:16 AM
Last modification on : Tuesday, November 16, 2021 - 4:30:36 AM
Long-term archiving on: : Thursday, May 13, 2021 - 6:33:29 PM


Files produced by the author(s)


  • HAL Id : hal-03139586, version 1



Gabriel Frisch, Jean-Benoist Leger, Yves Grandvalet. SparseBM: A Python Module for Handling Sparse Graphs with Block Models. 2021. ⟨hal-03139586⟩



Record views


Files downloads