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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'>bbv(1)</li>
<li class='tc'>www.complex-networks.net</li>
<li class='tr'>bbv(1)</li>
</ol>
<h2 id="NAME">NAME</h2>
<p class="man-name">
<code>bbv</code> - <span class="man-whatis">Grow a weighted scale-free random graph</span>
</p>
<h2 id="SYNOPSIS">SYNOPSIS</h2>
<p><code>bbv</code> <var>N</var> <var>m</var> <var>n0</var> <var>w0</var> <var>delta</var></p>
<h2 id="DESCRIPTION">DESCRIPTION</h2>
<p><code>bbv</code> grows an undirected weighted random scale-free graph with <var>N</var>
nodes using the model proposed by Barrat, Barthelemy, and
Vespignani. The initial network is a clique of <var>n0</var> nodes, and each
new node creates <var>m</var> new edges, each with weight <var>w0</var>. The parameter
<var>delta</var> sets the amount of weight to be redistributed in the
neighbourhood of newly-connected nodes.</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>
<dt class="flush"><var>w0</var></dt><dd><p> Weight of each new edge (must be >=0)</p></dd>
<dt class="flush"><var>delta</var></dt><dd><p> The amount of weight to be redistributed among the neighbours of
newly-connected nodes.</p></dd>
</dl>
<h2 id="OUTPUT">OUTPUT</h2>
<p><code>bbv</code> prints on STDOUT the edge list of the final graph, which
consists of three columns:</p>
<pre><code> node1 node2 weight
</code></pre>
<p>where <code>weight</code> is the weight of the corresponding edge. Please note
that each edge is printed only once.</p>
<h2 id="EXAMPLES">EXAMPLES</h2>
<p>The following command:</p>
<pre><code> $ bbv 10000 3 5 1.0 0.5 > bbv_10000_3_5_1.0_0.5.txt
</code></pre>
<p>creates a weighted 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. Each new edge has an initial weight equal to
<var>w0</var>. The weights of existing edges are rearranged after the addition
of a new edge, by rearranging an amount of weight equal to <var>delta</var>.
The final graph is saved in the file <code>bbv_10000_3_5_1.0_0.5.txt</code>
(notice the STDOUT redirection operator <code>></code>).</p>
<h2 id="SEE-ALSO">SEE ALSO</h2>
<p><a class="man-ref" href="ba.1.html">ba<span class="s">(1)</span></a>, <a class="man-ref" href="bb_fitness.1.html">bb_fitness<span class="s">(1)</span></a>, <a class="man-ref" href="dms.1.html">dms<span class="s">(1)</span></a></p>
<h2 id="REFERENCES">REFERENCES</h2>
<ul>
<li><p>A. Barrat, M. Barthelemy, and A. Vespignani. "Weighted Evolving
Networks: Coupling Topology and Weight Dynamics".
Phys. Rev. Lett. 92 (2004), 228701.</p></li>
<li><p>A. Barrat, M. Barthelemy, and A. Vespignani. "Modeling the
evolution of weighted networks". Phys. Rev. E 70 (2004), 066149.</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'>bbv(1)</li>
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