graph-ensembles 0.0.2

The graph ensemble package contains a set of methods to build fitness based graph ensembles from marginal information.

Graph ensembles

The graph ensemble package contains a set of methods to build fitness based graph ensembles from marginal information. These methods can be used to build randomized ensembles preserving the marginal information provided.

• Free software: GNU General Public License v3

• Documentation: https://graph-ensembles.readthedocs.io.

Installation

Install using:

pip install graph_ensembles

Usage

Currently only the StripeFitnessModel is fully implemented. An example of how it can be used is the following. For more see the example notebooks in the examples folder.

# Define graph marginals
 out_strength = np.array([[0, 0, 2],
                         [1, 1, 5],
                         [2, 2, 6],
                         [3, 2, 1]])

 in_strength = np.array([[0, 1, 5],
                         [0, 2, 4],
                         [1, 2, 3],
                         [3, 0, 2]])

 num_nodes = 4
 num_links = np.array([1, 1, 3])

 # Initialize model
 model = ge.StripeFitnessModel(out_strength, in_strength, num_links)

 # Fit model parameters
 model.fit()

 # Return probability matrix
 prob_mat = model.probability_matrix

Development

Please work on a feature branch and create a pull request to the development branch. If necessary to merge manually do so without fast forward:

git merge --no-ff myfeature

To build a development environment run:

python3 -m venv venv
source venv/bin/activate
pip install -e '.[dev]'

For testing:

pytest --cov

Credits

This is a project by Leonardo Niccolò Ialongo and Emiliano Marchese, under the supervision of Diego Garlaschelli.

History

0.0.2 (2020-11-13)

• Added steps for CI.

• Corrected broken links.

• Removed support for python 3.5 and 3.6

0.0.1 (2020-10-28)

• First release on PyPI. StripeFitnessModel available, all other model classes still dummies.