{% extends "layout.html" %}
{% set title = 'Overview' %}
{% block body %}


<blockquote>
<em> This documentation is currently being updated for release 0.99 of
  NetworkX. This update includes significant changes to the underlying
  Graph and DiGraph objects to reflect our common use case of weighted
  graphs and to improve performance.  See the
  <a href="{{ pathto("reference/api_changes") }}">API changes</a>
  for detailed information.
</em>
</blockquote>



<h2>High productivity software for complex networks</a></h2>

<img align="right" src="{{ pathto("_static/art1.png", 1) }}" alt="NetworkX art" />

<blockquote>
<p>NetworkX is a Python package for the creation, manipulation, and
study of the structure, dynamics, and functions of complex networks.</p>
</blockquote>

<h2>Quick Example</h2>


<table align="center" style="margin-left: 0px"><tr>
    <td width="50%">
<blockquote>
<pre>
&gt;&gt;&gt; import networkx as nx
&gt;&gt;&gt; G=nx.Graph()
&gt;&gt;&gt; G.add_edge(1,2)
&gt;&gt;&gt; G.add_node(&quot;spam&quot;)
&gt;&gt;&gt; print G.nodes()
[1, 2, 'spam']
&gt;&gt;&gt; print G.edges()
[(1, 2)]
</pre>
    </td>
    <td width="50%">
</td></tr>
</blockquote>
</table>


<h2>Documentation</h2>

<table class="contentstable" align="center" style="margin-left: 30px"><tr>
  <td width="50%">

    <p class="biglink"><a class="biglink" href="{{ pathto("tutorial/index") }}">Tutorial</a><br/>
       <span class="linkdescr">start here</span></p>
    <p class="biglink"><a class="biglink" href="{{ pathto("reference/index") }}">Reference</a><br/>
       <span class="linkdescr">guide to all functions and classes</span></p>
    <p class="biglink"><a class="biglink" href="{{ pathto("examples/index") }}">Examples</a><br/>
       <span class="linkdescr">using the library</span></p>
    <p class="biglink"><a class="biglink" href="{{ pathto("gallery") }}">Gallery</a><br/>
       <span class="linkdescr">network drawings</span></p>
  </td>
  <td width="50%">
    <p class="biglink"><a class="biglink" href="{{ pathto("contents") }}">Contents</a><br/>
       <span class="linkdescr">a complete overview</span></p>
    <p class="biglink"><a class="biglink" href="{{ pathto("search") }}">Search Page</a><br/>
       <span class="linkdescr">search the documentation</span></p>
    <p class="biglink"><a class="biglink" href="{{ pathto("genindex") }}">General Index</a><br/>
       <span class="linkdescr">all functions, classes, terms</span></p>
    <p class="biglink"><a class="biglink" href="{{ pathto("modindex") }}">Module Index</a><br/>
       <span class="linkdescr">quick access to all documented modules</span></p>
  </td></tr>
</table>

<h2>Features:</h2>
<blockquote>
<ul class="simple">
<li>Includes standard graph-theoretic and statistical physics functions</li>
<li>Easy exchange of network algorithms between applications,
disciplines, and platforms</li>
<li>Includes many classic graphs and synthetic networks</li>
<li>Nodes and edges can be &quot;anything&quot;
(e.g. time-series, text, images, XML records)</li>

<li>Exploits existing code from high-quality legacy software in C,
C++, Fortran, etc.</li>
<li>Open source (encourages community input)</li>
<li>Unit-tested</li>
</ul>

<p>Additional benefits due to Python:</p>

<ul class="simple">
<li>Allows fast prototyping of new algorithms</li>
<li>Easy to teach</li>
<li>Multi-platform</li>

<li>Allows easy access to almost any database</li>
</ul>
</blockquote>

  <h2>Get NetworkX</h2>
<blockquote>
  <p>
    NetworkX is available as an <a
    href="http://peak.telecommunity.com/DevCenter/EasyInstall">easy-install</a>able
    package on the <a href="http://pypi.python.org/pypi/networkx">Python Package
    Index</a>.
  </p>
  <p>The code can be found on the NetworkX SVN server at
    <tt><a href="http://networkx.lanl.gov/svn/networkx/trunk">http://networkx.lanl.gov/svn/networkx/trunk</a></tt>.</p>
</blockquote>

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