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<a href="#EXAMPLES">EXAMPLES</a>
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<ol class='man-decor man-head man head'>
<li class='tl'>hv_net(1)</li>
<li class='tc'>www.complex-networks.net</li>
<li class='tr'>hv_net(1)</li>
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<h2 id="NAME">NAME</h2>
<p class="man-name">
<code>hv_net</code> - <span class="man-whatis">Sample a random graph with an assigned joint degree distribution</span>
</p>
<h2 id="SYNOPSIS">SYNOPSIS</h2>
<p><code>hv_net</code> <var>graph_in</var> [SHOW]</p>
<h2 id="DESCRIPTION">DESCRIPTION</h2>
<p><code>hv_net</code> samples a random graph whose joint degree distribution is
equal to that of another graph provided as input, using the
hidden-variable model proposed by Boguna ans Pastor-Satorras.</p>
<h2 id="PARAMETERS">PARAMETERS</h2>
<dl>
<dt><var>graph_in</var></dt><dd><p> File containing the edge list of the existing graph. If equal to
'-' (dash), read the edge list from STDIN.</p></dd>
<dt class="flush">SHOW</dt><dd><p> If the second parameter is equal to <code>SHOW</code>, the program prints on
STDERR the hidden variable and actual degree of each node.</p></dd>
</dl>
<h2 id="EXAMPLES">EXAMPLES</h2>
<p>Let us assume that we want to create a graph whose joint degree
distribution is equal to that of the graph contained in
<code>AS-20010316.net</code> (i.e., the graph of the Internet at the AS level in
March 2001). We can use the command:</p>
<pre><code> $ hv_net AS-20010316.net > AS-20010316.net_rand
</code></pre>
<p>which will sample a random graph with the same joint-degree
distribution and will save its edge list in the file
<code>AS-20010316.net_rand</code> (notice the STDOUT redirection operator
<code>></code>). Additionally, we can also save the values of the hidden
variables and actual degrees of the nodes by specifying <code>SHOW</code> as a
second parameter:</p>
<pre><code> $ hv_net AS-20010316.net SHOW > AS-20010316.net_rand 2>AS-20010316.net_rand_hv
</code></pre>
<p>In this case, the file <code>AS-20010316.net_rand_hv</code> will contain the
values of the hidden variable of each node and of the actual degree of
the node in the sampled graph, in the format:</p>
<pre><code> h1 k1
h2 k2
....
</code></pre>
<h2 id="SEE-ALSO">SEE ALSO</h2>
<p><a class="man-ref" href="conf_model_deg.1.html">conf_model_deg<span class="s">(1)</span></a>, <a class="man-ref" href="conf_model_deg_nocheck.1.html">conf_model_deg_nocheck<span class="s">(1)</span></a></p>
<h2 id="REFERENCES">REFERENCES</h2>
<ul>
<li><p>M. Boguna and R. Pastor-Satorras. "Class of correlated random
networks with hidden variables". Phys. Rev. E 68 (2003), 036112.</p></li>
<li><p>V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles,
Methods and Applications", Chapter 7, Cambridge University Press
(2017)</p></li>
<li><p>V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles,
Methods and Applications", Appendix 14, Cambridge University Press
(2017)</p></li>
</ul>
<h2 id="AUTHORS">AUTHORS</h2>
<p>(c) Vincenzo 'KatolaZ' Nicosia 2009-2017 <code><v.nicosia@qmul.ac.uk></code>.</p>
<ol class='man-decor man-foot man foot'>
<li class='tl'>www.complex-networks.net</li>
<li class='tc'>September 2017</li>
<li class='tr'>hv_net(1)</li>
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