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ForceAtlas2 for Python and NetworkX | ||
=================================== | ||
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A port of Gephi’s Force Atlas 2 layout algorithm to Python 2 and Python | ||
3 (with a wrapper for NetworkX). This is the fastest python | ||
implementation available with most of the features complete. It also | ||
supports Barnes Hut approximation for maximum speedup. | ||
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The implementation is based on this `paper`_ and the corresponding | ||
`gephi-java-code`_. ForceAtlas2 algorithm is really fast compared to the | ||
fruchterman reingold algorithm (spring layout) of networkx and scales | ||
well to high number of nodes (>10000). | ||
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Installation | ||
------------ | ||
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Install from pip: | ||
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:: | ||
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pip install fa2 | ||
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To build and install run from source: | ||
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:: | ||
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python setup.py install | ||
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Cython is highly recommended as it will speed up by a factor of 10-100x depending on the graph | ||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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Dependencies | ||
~~~~~~~~~~~~ | ||
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- numpy (adjacency matrix as complete matrix) | ||
- scipy (adjacency matrix as sparse matrix) | ||
- tqdm (progressbar) | ||
- cython (10-100x speedup) | ||
- networkx (To use the NetworkX wrapper function, you obviously need | ||
NetworkX) | ||
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Usage | ||
----- | ||
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from fa2 import ForceAtlas2 | ||
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Create a ForceAtlas2 object with the appropriate settings. ForceAtlas2 | ||
class contains two important methods: | ||
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.. code:: python | ||
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 | ||
.. code:: python | ||
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. | ||
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.. code-block:: python | ||
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. | ||
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Features Completed | ||
------------------ | ||
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- **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” | ||
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Documentation | ||
------------- | ||
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You will find all the documentation in the source code | ||
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Contributors | ||
------------ | ||
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Contributions are highly welcome. Please submit your pull requests and | ||
become a collaborator. | ||
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Copyright | ||
--------- | ||
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Copyright (C) 2017 Bhargav Chippada [email protected]. | ||
Licensed under the GNU GPLv3. | ||
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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: | ||
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:: | ||
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Copyright 2008-2011 Gephi | ||
Authors : Mathieu Jacomy <[email protected]> | ||
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. | ||
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<https://github.com/mwshinn/forceatlas2-python> | ||
Copyright 2016 Max Shinn <[email protected]> | ||
Available under the GPLv3 | ||
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Also, thanks to Eugene Bosiakov <https://github.com/bosiakov/fa2l> | ||
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.. _paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098679 | ||
.. _gephi-java-code: https://github.com/gephi/gephi/blob/master/modules/LayoutPlugin/src/main/java/org/gephi/layout/plugin/forceAtlas2/ForceAtlas2.java |