nx-parallel is a NetworkX backend that uses joblib for parallelization. This project aims to provide parallelized implementations of various NetworkX functions to improve performance.
nx-parallel provides parallelized implementations for the following NetworkX functions:
- betweeness_centrality
- local_efficiency
- number_of_isolates
- all_pairs_bellman_ford_path
- is_reachable
- tournament_is_strongly_connected
- closeness_vitality
See the /timing folder for more heatmaps and code for heatmap generation!
To setup a local development:
- Fork this repository.
- Clone the forked repository locally.
git clone git@github.com:<your_username>/networkx.git
- Create a fresh conda/mamba virtualenv and install the dependencies
pip install -e ".[developer]"
- Install pre-commit actions that will run the linters before making a commit
pre-commit install
Here's an example of how to use nx-parallel:
import networkx as nx
import nx_parallel
G = nx.path_graph(4)
H = nx_parallel.ParallelGraph(G)
nx.betweenness_centrality(H)
# output : {0: 0.0, 1: 0.6666666666666666, 2: 0.6666666666666666, 3: 0.0}To run tests for the project, use the following command:
PYTHONPATH=. \
NETWORKX_TEST_BACKEND=parallel \
NETWORKX_FALLBACK_TO_NX=True \
pytest --pyargs networkx "$@"
We'd love to have you contribute to nx-parallel! Here are some guidelines on how to do that:
- Issues: Feel free to open issues for any problems you face, or for new features you'd like to see implemented.
- Pull requests: If you'd like to implement a feature or fix a bug yourself, we'd be happy to review a pull request. Please make sure to explain the changes you made in the pull request description.
This project is part of the larger NetworkX project. If you're interested in contributing to NetworkX, you can find more information in the NetworkX contributing guidelines.