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<title>ba(1) - Grow a Barabasi-Albert scale-free random graph</title>
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<a href="#NAME">NAME</a>
<a href="#SYNOPSIS">SYNOPSIS</a>
<a href="#DESCRIPTION">DESCRIPTION</a>
<a href="#PARAMETERS">PARAMETERS</a>
<a href="#OUTPUT">OUTPUT</a>
<a href="#EXAMPLES">EXAMPLES</a>
<a href="#SEE-ALSO">SEE ALSO</a>
<a href="#REFERENCES">REFERENCES</a>
<a href="#AUTHORS">AUTHORS</a>
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<ol class='man-decor man-head man head'>
<li class='tl'>ba(1)</li>
<li class='tc'>www.complex-networks.net</li>
<li class='tr'>ba(1)</li>
</ol>
<h2 id="NAME">NAME</h2>
<p class="man-name">
<code>ba</code> - <span class="man-whatis">Grow a Barabasi-Albert scale-free random graph</span>
</p>
<h2 id="SYNOPSIS">SYNOPSIS</h2>
<p><code>ba</code> <var>N</var> <var>m</var> <var>n0</var></p>
<h2 id="DESCRIPTION">DESCRIPTION</h2>
<p><code>ba</code> grows an undirected random scale-free graph with <var>N</var> nodes using
the linear preferential attachment model proposed by Barabasi and
Albert. The initial network is a ring of <var>n0</var> nodes, and each new node
creates <var>m</var> new edges. The resulting graph will have a scale-free
degree distribution, whose exponent converges to <code>gamma=3.0</code> for large
<var>N</var>.</p>
<h2 id="PARAMETERS">PARAMETERS</h2>
<dl>
<dt class="flush"><var>N</var></dt><dd><p> Number of nodes of the final graph.</p></dd>
<dt class="flush"><var>m</var></dt><dd><p> Number of edges created by each new node.</p></dd>
<dt class="flush"><var>n0</var></dt><dd><p> Number of nodes in the initial (seed) graph.</p></dd>
</dl>
<h2 id="OUTPUT">OUTPUT</h2>
<p><code>ba</code> prints on STDOUT the edge list of the final graph.</p>
<h2 id="EXAMPLES">EXAMPLES</h2>
<p>The following command:</p>
<pre><code> $ ba 10000 3 5 > ba_10000_3_5.txt
</code></pre>
<p>creates a Barabasi-Albert scale-free graph with <var>N=10000</var> nodes, where
each new node creates <var>m=3</var> new edges and the initial seed network is
a ring of <var>n0=5</var> nodes. The edge list of the graph is saved in the
file <code>ba_10000_3_5.txt</code> (thanks to the redirection operator <code>></code>).</p>
<h2 id="SEE-ALSO">SEE ALSO</h2>
<p><span class="man-ref">bb_fitness<span class="s">(1)</span></span>, <span class="man-ref">dms<span class="s">(1)</span></span>, <span class="man-ref">bbv<span class="s">(1)</span></span></p>
<h2 id="REFERENCES">REFERENCES</h2>
<ul>
<li><p>A.-L. Barabasi, R. Albert, "Emergence of scaling in random
networks", Science 286, 509-512 (1999).</p></li>
<li><p>V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles,
Methods and Applications", Chapter 6, Cambridge University Press
(2017)</p></li>
<li><p>V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles,
Methods and Applications", Appendix 13, 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'>ba(1)</li>
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