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Scikit-Network is an open-source python package created at LINCS.

You are very welcome to participate and add your own graph algorithms.

We just ask you to follow some guidelines:

Allowed external dependencies

• NumPy
• SciPy
• Cython

Coding style

• Object oriented programming with Scikit-learn style: an algorithm is a class with a fit method.
• PEP8 coding style (it is recommended to use Pycharm Community as an IDE)
• Explicit inputs / outputs: adjacency, biadjacency, labels, scores, etc.

Documentation

• NumPy docstring

labels: np.ndarray(dtype=int)
vector of labels

• The description of each function or class should begin with a verb (imperative form) or a noun.

Cluster nodes of the graph.

• The description of each function or class should include an example, in the following form:

imports
algo
data
output

• Reference style: APA

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment.