Welcome to ModularityPruning’s documentation!

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ModularityPruning is a pruning tool to identify small subsets of network partitions that are significant from the perspective of stochastic block model inference. It:

  • Works for single-layer and multi-layer networks

  • Supports both directed and undirected networks

  • Supports weighted networks via the weight edge attribute

  • Restricts focus to a fixed number of communities when desired

  • Contains a faster and more robust implementation of the CHAMP (Convex Hull of Admissible Modularity Partitions) algorithm (see CHAMP_2D and CHAMP_3D)

Additional information can be found in the journal article at https://doi.org/10.1038/s41598-022-20142-6 or the article’s significantly more detailed Supplementary Information.

Note: support for weighted and directed networks is an extension beyond the original publication.

Prior to version v1.3.0, we used the Louvain algorithm for modularity maximization instead of Leiden. The deprecated module modularitypruning.louvain_utilities now shims functions to their corresponding Leiden versions in modularitypruning.leiden_utilities.

Contents:

Indices and tables