https://github.com/ryanrudes/forceatlas

Gephi ForceAtlas2 with networkx compatibility and support for thread-based parallelism
https://github.com/ryanrudes/forceatlas

force-directed-graphs forceatlas2 graph-layout multithreading networkx

Last synced: 8 months ago
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Gephi ForceAtlas2 with networkx compatibility and support for thread-based parallelism

• Host: GitHub
• URL: https://github.com/ryanrudes/forceatlas
• Owner: ryanrudes
• License: gpl-3.0
• Created: 2022-07-23T19:03:43.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-07-16T22:25:11.000Z (almost 2 years ago)
• Last Synced: 2025-10-14T05:57:56.568Z (8 months ago)
• Topics: force-directed-graphs, forceatlas2, graph-layout, multithreading, networkx
• Language: Python
• Homepage:
• Size: 57 MB
• Stars: 4
• Watchers: 1
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Gephi ForceAtlas2

[![PyPI download month](https://img.shields.io/pypi/dm/forceatlas.svg)](https://pypi.python.org/pypi/forceatlas/)

[![PyPI - Status](https://img.shields.io/pypi/status/forceatlas)](https://pypi.python.org/pypi/forceatlas/)

[![PyPI](https://img.shields.io/pypi/v/forceatlas)](https://pypi.python.org/pypi/forceatlas/)

![GitHub](https://img.shields.io/github/license/ryanrudes/forceatlas)

This package is a Python-friendly port of the multithreaded [Java implementation](https://github.com/klarman-cell-observatory/forceatlas2) of the Gephi ForceAtlas2 layout algorithm. It is compatible with [networkx](https://github.com/networkx/networkx) and supports both 2D and 3D layout.

## Installation

It can be installed with the default Python package manager via the command

```

pip install forceatlas

```

## Example

The package is consistent with networkx in documentation-style and function arguments. See the documentation for more details.

```python

import matplotlib.pyplot as plt

import forceatlas as fa2

import networkx as nx

G = nx.fast_gnp_random_graph(100, 0.1)

pos = fa2.fa2_layout(G, iterations = 10000, threshold = 1e-3)

nx.draw(G)

plt.savefig("graph.png")

```