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  <div class="section" id="normalized-laplacian-matrix">
<h1>normalized_laplacian_matrix<a class="headerlink" href="#normalized-laplacian-matrix" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="networkx.linalg.laplacianmatrix.normalized_laplacian_matrix">
<tt class="descname">normalized_laplacian_matrix</tt><big>(</big><em>G</em>, <em>nodelist=None</em>, <em>weight='weight'</em><big>)</big><a class="reference internal" href="../../_modules/networkx/linalg/laplacianmatrix.html#normalized_laplacian_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#networkx.linalg.laplacianmatrix.normalized_laplacian_matrix" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the normalized Laplacian matrix of G.</p>
<p>The normalized graph Laplacian is the matrix</p>
<div class="math">
<p><span class="math">NL = D^{-1/2} L D^{-1/2}</span></p>
</div><p>where <span class="math">L</span> is the graph Laplacian and <span class="math">D</span> is the diagonal matrix of
node degrees.</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> : graph</p>
<blockquote>
<div><p>A NetworkX graph</p>
</div></blockquote>
<p><strong>nodelist</strong> : list, optional</p>
<blockquote>
<div><p>The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().</p>
</div></blockquote>
<p><strong>weight</strong> : string or None, optional (default=&#8217;weight&#8217;)</p>
<blockquote>
<div><p>The edge data key used to compute each value in the matrix.
If None, then each edge has weight 1.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>L</strong> : NumPy matrix</p>
<blockquote class="last">
<div><p>The normalized Laplacian matrix of G.</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.linalg.laplacianmatrix.laplacian_matrix.html#networkx.linalg.laplacianmatrix.laplacian_matrix" title="networkx.linalg.laplacianmatrix.laplacian_matrix"><tt class="xref py py-obj docutils literal"><span class="pre">laplacian_matrix</span></tt></a></p>
</div>
<p class="rubric">Notes</p>
<p>For MultiGraph/MultiDiGraph, the edges weights are summed.
See to_numpy_matrix for other options.</p>
<p>If the Graph contains selfloops, D is defined as diag(sum(A,1)), where A is
the adjencency matrix <a class="reference internal" href="#r295">[R295]</a>.</p>
<p class="rubric">References</p>
<table class="docutils citation" frame="void" id="r294" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id2">[R294]</a></td><td>Fan Chung-Graham, Spectral Graph Theory,
CBMS Regional Conference Series in Mathematics, Number 92, 1997.</td></tr>
</tbody>
</table>
<table class="docutils citation" frame="void" id="r295" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label">[R295]</td><td><em>(<a class="fn-backref" href="#id1">1</a>, <a class="fn-backref" href="#id3">2</a>)</em> Steve Butler, Interlacing For Weighted Graphs Using The Normalized
Laplacian, Electronic Journal of Linear Algebra, Volume 16, pp. 90-98,
March 2007.</td></tr>
</tbody>
</table>
</dd></dl>

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