Skip to main content

Force Atlas 2 graph layout

Project description

ForceAtlas2 is a continuous graph layout algorithm for handy network visualization.

This implementation is based on this paper.

Warning: Some features (especially Prevent Overlapping) are not completely implemented. I’m waiting for your pull-requests.

Example of social graph rendered with force atlas 2 layout:

https://raw.githubusercontent.com/bosiakov/fa2l/master/_static/result.jpg

Installing

Supports Python 3.3+

Install from pip:

pip install fa2l

To build and install run from source:

python setup.py install

Usage

import networkx as nx
from fa2l import force_atlas2_layout
import matplotlib.pyplot as plt

G = nx.erdos_renyi_graph(100, 0.15, directed=False)

positions = force_atlas2_layout(G,
                                iterations=1000,
                                pos_list=None,
                                node_masses=None,
                                outbound_attraction_distribution=False,
                                lin_log_mode=False,
                                prevent_overlapping=False,
                                edge_weight_influence=1.0,

                                jitter_tolerance=1.0,
                                barnes_hut_optimize=True,
                                barnes_hut_theta=0.5,

                                scaling_ratio=2.0,
                                strong_gravity_mode=False,
                                multithread=False,
                                gravity=1.0)

nx.draw_networkx(G, positions, cmap=plt.get_cmap('jet'), node_size=50, with_labels=False)
plt.show()

Features

Force Atlas 2 features these settings:

  • Approximate Repulsion: Barnes Hut optimization: n² complexity to n.ln(n).
  • Gravity: Attracts nodes to the center. Prevents islands from drifting away.
  • Dissuade Hubs: Distributes attraction along outbound edges. Hubs attract less and thus are pushed to the borders.
  • LinLog mode: Switch ForceAtlas model from lin-lin to lin-log. Makes clusters more tight.
  • Prevent Overlap. WARNING! Does not work very well.
  • Tolerance: How much swinging you allow. Above 1 discouraged. Lower gives less speed and more precision.
  • Edge Weight Influence: How much influence you give to the edges weight. 0 is “no influence” and 1 is “normal”.

Documentation

You will find all the documentation in the source code

Project details


Release history Release notifications

This version
History Node

0.2

History Node

0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
fa2l-0.2.tar.gz (7.8 kB) Copy SHA256 hash SHA256 Source None Sep 6, 2017

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page