| Autor: |
Frisch, Gabriel, Leger, Jean-Benoist, Grandvalet, Yves |
| Přispěvatelé: |
Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), Frisch, Gabriel |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Popis: |
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). |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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