fa2 0.2

Installation

Install from pip:

pip install fa2

To build and install run from source:

python setup.py install

Cython is highly recommended if you are buidling from source as it will speed up by a factor of 10-100x depending on the graph

Usage

from fa2 import ForceAtlas2

Create a ForceAtlas2 object with the appropriate settings. ForceAtlas2 class contains two important methods:

forceatlas2 (G, pos, iteraions)
# G is a graph in 2D numpy ndarray format (or) scipy sparse matrix format
# pos is a numpy array (Nx2) of initial positions of nodes
# iterations is num of iterations to run the algorithm

forceatlas2_networkx_layout(G, pos, iterations)
# G is networkx graph
# pos is a dictionary, as in networkx
# iterations is num of iterations to run the algorithm

Below is an example usage. You can also see the feature settings of ForceAtlas2 class.

import networkx as nx
from fa2 import ForceAtlas2
import matplotlib.pyplot as plt

G = nx.karate_club_graph()

forceatlas2 = ForceAtlas2(
                          # Behavior alternatives
                          outboundAttractionDistribution=False,  # Dissuade hubs
                          linLogMode=False,  # NOT IMPLEMENTED
                          adjustSizes=False,  # Prevent overlap (NOT IMPLEMENTED)
                          edgeWeightInfluence=1.0,

                          # Performance
                          jitterTolerance=1.0,  # Tolerance
                          barnesHutOptimize=True,
                          barnesHutTheta=1.2,
                          multiThreaded=False,  # NOT IMPLEMENTED

                          # Tuning
                          scalingRatio=2.0,
                          strongGravityMode=False,
                          gravity=1.0,

                          # Log
                          verbose=True)

positions = forceatlas2.forceatlas2_networkx_layout(G, pos=None, iterations=2000)
nx.draw_networkx(G, positions, cmap=plt.get_cmap('jet'), node_size=50, with_labels=False)
plt.show()

You can also take a look at forceatlas2.py file for understanding the ForceAtlas2 class and its functions better.

Features Completed

• barnesHutOptimize: 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

• scalingRatio: How much repulsion you want. More makes a more sparse graph

• strongGravityMode: A stronger gravity view

• jitterTolerance: How much swinging you allow. Above 1 discouraged. Lower gives less speed and more precision

• verbose: Shows a progressbar of iterations completed. Also, shows time taken for different force computations

• edgeWeightInfluence: 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

Contributors

Contributions are highly welcome. Please submit your pull requests and become a collaborator.

Copyright

Copyright (C) 2017 Bhargav Chippada bhargavchippada19@gmail.com.
Licensed under the GNU GPLv3.

The files are heavily based on the java files included in Gephi, git revision 2b9a7c8 and Max Shinn’s port to python of the algorithm. Here I include the copyright information from those files:

Copyright 2008-2011 Gephi
Authors : Mathieu Jacomy <mathieu.jacomy@gmail.com>
Website : http://www.gephi.org
Copyright 2011 Gephi Consortium. All rights reserved.
Portions Copyrighted 2011 Gephi Consortium.
The contents of this file are subject to the terms of either the
GNU General Public License Version 3 only ("GPL") or the Common
Development and Distribution License("CDDL") (collectively, the
"License"). You may not use this file except in compliance with
the License.

<https://github.com/mwshinn/forceatlas2-python>
Copyright 2016 Max Shinn <mws41@cam.ac.uk>
Available under the GPLv3

Also, thanks to Eugene Bosiakov <https://github.com/bosiakov/fa2l>