A modularity based tool for screening a set of partitions.

The CHAMP python package provides two levels of functionality:

• Identifying the subset of partitions from a group of partitions (regardless of how they were discovered) with optimal modularity. See Running.
• Parallelized implementation of modularity based community detection method, louvain with efficient filtering (ala CHAMP), management, and storage of the generated partitions. See Louvain Parallel Extension .

For complete documentation, please visit our ReadTheDocs page: http://champ.readthedocs.io/en/latest/

The CHAMP module is hosted on PyPi. The easiest way to install is via the pip command:

pip install champ

For installation from source, the latest version of champ can be downloaded from GitHub:

https://github.com/wweir827/CHAMP

For basic installation:

python setup.py install

Most of the dependencies for CHAMP are fairly standard tools for data analysis in Python, with the exception of louvain_igraph. They include :

• NumPy : Python numerical analysis library.
• sklearn :Machine learning tools for python.
• python-igraph :igraph python version for manipulation of networks.
• matplotlib :Python data visualization library.
• louvain :Vincent Traag's implementation of louvain algorithm.
• h5py : HDF5 file format library for python.

These should all be handled automatically if using pip to install.

Please cite:

bibtex

For more details and results see our preprint

This project was supported by the James S. McDonnell Foundation 21st Century Science Initiative -Complex Systems Scholar Award grant #220020315, by the National Institutes of Health through Award Numbers R01HD075712, R56DK111930 and T32GM067553, and by the CDC Prevention Epicenter Program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.