Generalised Configuration Model random graphs in Python
Project description
gcmpy: Generalised Configuration Model random graphs in Python
Overview
gcmpy is a Python library that creates random graph models according to the generalised configuration model (GCM). Random graph models provide an excellent framework to integrate topology with dynamics. The topology of a network is crucial to the outcome of a dynamical process, such as an epidemic, occurring over a network.
To create the networks, gcmpy creates a joint degree distribution object through a variety of analytical or empirical methods. Once constructed, this joint distribution is sampled to obtain a joint degree sequence. The joint sequence is then used in the GCM algorithm to create a networkx graph. It can also be used to create an edge list directly, which is significantly faster.
There is also a tools library for obtaining useful quantities from the network as well as converting a joint degree distribution into excess joint degree distributions and vice versa, for example. We also provide an MCMC rewiring algorithm to stochastically rewire a synthetic network’s correlations structure to a target joint excess joint degree distributions.
Installation
You can install gcmpy directly from PyPi using pip:
pip install gcmpyThe master distribution of gcmpy is hosted on GitHub. To obtain a copy, just clone the repo:
git clone git@github.com:PeterStAndrews/gcmpy.git
cd gcmpy
python setup.py installThe unit tests can be discovered from the project root using
python3 -m unittest discover -v -s test/ -p 'test_*.py'Documentation
API documentation for gcmpy is available on ReadTheDocs
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