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Pré-Publication, Document De Travail Année : 2021

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

Résumé

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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Dates et versions

hal-03139586 , version 1 (12-02-2021)

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  • HAL Id : hal-03139586 , version 1

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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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