modularitypruning 1.2.1

Pruning tool to identify small subsets of network partitions that are significant from the perspective of stochastic block model inference.

ModularityPruning

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 preprint at https://doi.org/10.21203/rs.3.rs-1551680/v1 for more information.

Installation

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

Basic Usage

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.louvain_utilities import repeated_louvain_from_gammas
import numpy as np

# get Karate Club graph in igraph
G = ig.Graph.Famous("Zachary")

# run louvain 1000 times on this graph from gamma=0 to gamma=2
partitions = repeated_louvain_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.

More Information

The TODO items and figure generation runtimes READMEs may also be of interest.