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<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>


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<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>
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    <td width="50%">
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</blockquote>
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<h2>Documentation</h2>

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    <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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