https://github.com/ragibson/modularitypruning

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.
https://github.com/ragibson/modularitypruning

community-detection graph-theory modularity multilayer-networks network-graph network-science pruning stochastic-block-model

Last synced: 11 months ago
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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.

• Host: GitHub
• URL: https://github.com/ragibson/modularitypruning
• Owner: ragibson
• License: mit
• Created: 2020-02-06T21:40:20.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2024-10-29T03:11:38.000Z (over 1 year ago)
• Last Synced: 2024-10-29T03:18:17.128Z (over 1 year ago)
• Topics: community-detection, graph-theory, modularity, multilayer-networks, network-graph, network-science, pruning, stochastic-block-model
• Language: Python
• Homepage:
• Size: 2.68 MB
• Stars: 16
• Watchers: 4
• Forks: 2
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# ModularityPruning

![ModularityPruning visualization](docs/example_figures/article_figure_method_visual.png)

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](https://modularitypruning.readthedocs.io/en/latest/)

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](

https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-022-20142-6/MediaObjects/41598_2022_20142_MOESM1_ESM.pdf

).

## Installation

This project is on [PyPI](https://pypi.org/project/modularitypruning/) 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

```python

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.

## More Information

The [issues (which contains some potential future work)](https://github.com/ragibson/ModularityPruning/issues)

and [figure generation runtimes README](figure_generation_runtimes.md) may also

be of interest.