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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).
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-03139586
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

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

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