CDlib is a meta-library for community discovery in complex networks: it implements algorithms, clustering fitness functions as well as visualization facilities.
CDlib is designed around the networkx python library: however, when needed, it takes care to automatically convert (from and to) igraph object so to provide an abstraction on specific algorithm implementations to the final user.
CDlib provides a standardized input/output facilities for several Community Discovery algorithms: whenever possible, to guarantee literature coherent results, implementations of CD algorithms are inherited from their original projects (see for instance Infomap,
Louvain, Leiden).
The original projects embedded in CDlib are acknowledged on the documentation website:
here the complete list of the CD methods included in the library.
If you use CDlib as support to your research consider citing:
G. Rossetti, L. Milli, R. Cazabet. CDlib: a Python Library to Extract, Compare and Evaluate Communities from Complex Networks. Applied Network Science Journal. 2019. DOI:10.1007/s41109-019-0165-9
Check out the official tutorial to get started!
If you would like to test CDlib functionalities without installing anything on your machine consider using the preconfigured Jupyter Hub instances offered by SoBigData++.
CDlib requires python>=3.6.
To install the latest version of our library just download (or clone) the current project, open a terminal and run the following commands:
pip install -r requirements.txt
pip install -r requirements_optional.txt # (Optional) this might not work in Windows systems due to C-based dependencies.
pip install .Alternatively use pip:
pip install cdlibCDlib relies on a few packages calling C code (namely: python-igraph, leidenalg, angel_cd and infomap).
The default installation will not set up such requirements since their configuration under non *nix systems is not trivial and cannot be easily automated.
Such a choice has been made to allow (even) Windows user to install the library and get access to its core functionalities.
To made available (most of) the optional packages you can either:
- (Windows) manually install the optional packages (versions details are specified in
requirements_optional.txt) following the original projects guidelines, or - (Linux/OSX) run the command:
pip install cdlib[C]Such caveat will install everything that can be easily automated under Linux/OSX.
The only optional dependency that will remain unsatisfied following the previous procedures will be graph-tool (used to add SBM models).
If you need it up and running, refer to the official documentation.
CDlib is an active project, any contribution is welcome!
If you like to include your model in CDlib feel free to fork the project, open an issue and contact us.
Contributing is good, doing it correctly is better! Check out our rules, issue a proper pull request /bug report / feature request.
We are a welcoming community... just follow the Code of Conduct.
