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<div class="section" id="degree-assortativity-coefficient">
<h1>degree_assortativity_coefficient<a class="headerlink" href="#degree-assortativity-coefficient" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="networkx.algorithms.assortativity.degree_assortativity_coefficient">
<tt class="descname">degree_assortativity_coefficient</tt><big>(</big><em>G</em>, <em>x='out'</em>, <em>y='in'</em>, <em>weight=None</em>, <em>nodes=None</em><big>)</big><a class="headerlink" href="#networkx.algorithms.assortativity.degree_assortativity_coefficient" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute degree assortativity of graph.</p>
<p>Assortativity measures the similarity of connections
in the graph with respect to the node degree.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>G</strong> : NetworkX graph</p>
<p><strong>x: string (‘in’,’out’)</strong> :</p>
<blockquote>
<div><p>The degree type for source node (directed graphs only).</p>
</div></blockquote>
<p><strong>y: string (‘in’,’out’)</strong> :</p>
<blockquote>
<div><p>The degree type for target node (directed graphs only).</p>
</div></blockquote>
<p><strong>weight: string or None, optional (default=None)</strong> :</p>
<blockquote>
<div><p>The edge attribute that holds the numerical value used
as a weight. If None, then each edge has weight 1.
The degree is the sum of the edge weights adjacent to the node.</p>
</div></blockquote>
<p><strong>nodes: list or iterable (optional)</strong> :</p>
<blockquote>
<div><p>Compute degree assortativity only for nodes in container.
The default is all nodes.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>r</strong> : float</p>
<blockquote class="last">
<div><p>Assortativity of graph by degree.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition-see-also admonition seealso">
<p class="first admonition-title">See also</p>
<p class="last"><a class="reference internal" href="networkx.algorithms.assortativity.attribute_assortativity_coefficient.html#networkx.algorithms.assortativity.attribute_assortativity_coefficient" title="networkx.algorithms.assortativity.attribute_assortativity_coefficient"><tt class="xref py py-obj docutils literal"><span class="pre">attribute_assortativity_coefficient</span></tt></a>, <a class="reference internal" href="networkx.algorithms.assortativity.numeric_assortativity_coefficient.html#networkx.algorithms.assortativity.numeric_assortativity_coefficient" title="networkx.algorithms.assortativity.numeric_assortativity_coefficient"><tt class="xref py py-obj docutils literal"><span class="pre">numeric_assortativity_coefficient</span></tt></a>, <tt class="xref py py-obj docutils literal"><span class="pre">neighbor_connectivity</span></tt>, <a class="reference internal" href="networkx.algorithms.assortativity.degree_mixing_dict.html#networkx.algorithms.assortativity.degree_mixing_dict" title="networkx.algorithms.assortativity.degree_mixing_dict"><tt class="xref py py-obj docutils literal"><span class="pre">degree_mixing_dict</span></tt></a>, <a class="reference internal" href="networkx.algorithms.assortativity.degree_mixing_matrix.html#networkx.algorithms.assortativity.degree_mixing_matrix" title="networkx.algorithms.assortativity.degree_mixing_matrix"><tt class="xref py py-obj docutils literal"><span class="pre">degree_mixing_matrix</span></tt></a></p>
</div>
<p class="rubric">Notes</p>
<p>This computes Eq. (21) in Ref. <a class="reference internal" href="#r134">[R134]</a> , where e is the joint
probability distribution (mixing matrix) of the degrees. If G is
directed than the matrix e is the joint probability of the
user-specified degree type for the source and target.</p>
<p class="rubric">References</p>
<table class="docutils citation" frame="void" id="r134" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label">[R134]</td><td><em>(<a class="fn-backref" href="#id1">1</a>, <a class="fn-backref" href="#id2">2</a>)</em> M. E. J. Newman, Mixing patterns in networks,
Physical Review E, 67 026126, 2003</td></tr>
</tbody>
</table>
<table class="docutils citation" frame="void" id="r135" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id3">[R135]</a></td><td>Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M.
Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).</td></tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">G</span><span class="o">=</span><span class="n">nx</span><span class="o">.</span><span class="n">path_graph</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">r</span><span class="o">=</span><span class="n">nx</span><span class="o">.</span><span class="n">degree_assortativity_coefficient</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="s">"</span><span class="si">%3.1f</span><span class="s">"</span><span class="o">%</span><span class="n">r</span><span class="p">)</span>
<span class="go">-0.5</span>
</pre></div>
</div>
</dd></dl>
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