ModularityPruning is a pruning tool to identify small subsets of network partitions that are significant from the perspective of stochastic block model inference. This method works for single-layer and multi-layer networks, as well as for restricting focus to a fixed number of communities when desired.
See the documentation or the journal article at https://doi.org/10.1038/s41598-022-20142-6 for more information.
Significantly more details can be found in the article's Supplementary Information.
This project is on PyPI and can be installed with
pip install modularitypruning
# OR
pip3 install modularitypruning
Alternatively, you can install it from this repository directly:
git clone https://github.com/ragibson/ModularityPruning
cd ModularityPruning
python3 setup.py install
This package interfaces directly with python-igraph. A simple example of its usage is
import igraph as ig
from modularitypruning import prune_to_stable_partitions
from modularitypruning.leiden_utilities import repeated_leiden_from_gammas
import numpy as np
# get Karate Club graph in igraph
G = ig.Graph.Famous("Zachary")
# run leiden 1000 times on this graph from gamma=0 to gamma=2
partitions = repeated_leiden_from_gammas(G, np.linspace(0, 2, 1000))
# prune to the stable partitions from gamma=0 to gamma=2
stable_partitions = prune_to_stable_partitions(G, partitions, 0, 2)
print(stable_partitions)This prints
[(0, 0, 0, 0, 1, 1, 1, 0, 2, 2, 1, 0, 0, 0, 2, 2, 1, 0, 2, 0, 2, 0, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 2, 2)]
which is the stable 4-community split of the Karate Club network.
The issues (which contains some potential future work) and figure generation runtimes README may also be of interest.