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 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.
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.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) and figure generation runtimes README may also be of interest.
1 maintainer
Metadata
1 maintainer
Release files for modularitypruning 1.5.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| modularitypruning-1.5.1.tar.gz | 31.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| modularitypruning-1.5.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 57.6 kB
Release files / modularitypruning-1.5.1.tar.gz
| Download URL | modularitypruning-1.5.1.tar.gz |
|---|---|
| Size | 31.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
ffb59e9cc331c33560dbc25d5f617a63588e152d21bf0655fb05f1b8383d2da5
|
|
BLAKE2b-256 checksum How to use checksums |
c2c7b61d6f1f5e5e86824c3967f82f9d87468fdd1bc1ec9265151f867e1854ae
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.3
|
Release files / modularitypruning-1.5.1-py3-none-any.whl
| Download URL | modularitypruning-1.5.1-py3-none-any.whl |
|---|---|
| Size | 25.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
7030518ebb8126dd4df88a8e8c0b4a08becd2d67d03f085cc6d1e66bb0be4ac9
|
|
BLAKE2b-256 checksum How to use checksums |
43f3408b30cd128bddecc36c7a75de88d7b7b2e88d4902cbbb6517319461cd98
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.3
|
