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<h1 class="title toc-ignore">igraph (interfaz R)</h1>


<div id="TOC">
<ul>
<li><a href="#instalaci%C3%B3n" id="toc-instalación">Instalación</a></li>
<li><a href="#uso-de-igraph" id="toc-uso-de-igraph">Uso de
igraph</a></li>
<li><a href="#crear-un-grafo" id="toc-crear-un-grafo">Crear un
grafo</a></li>
<li><a href="#ids-de-v%C3%A9rtices-y-aristas" id="toc-ids-de-vértices-y-aristas">IDs de vértices y aristas</a></li>
<li><a href="#a%C3%B1adir-y-borrar-v%C3%A9rtices-y-aristas" id="toc-añadir-y-borrar-vértices-y-aristas">Añadir y borrar vértices y
aristas</a></li>
<li><a href="#construcci%C3%B3n-de-grafos" id="toc-construcción-de-grafos">Construcción de grafos</a></li>
<li><a href="#establecer-y-recuperar-atributos" id="toc-establecer-y-recuperar-atributos">Establecer y recuperar
atributos</a></li>
<li><a href="#propiedades-estructurales-de-los-grafos" id="toc-propiedades-estructurales-de-los-grafos">Propiedades
estructurales de los grafos</a></li>
<li><a href="#b%C3%BAsqueda-de-v%C3%A9rtices-y-aristas-basada-en-atributos" id="toc-búsqueda-de-vértices-y-aristas-basada-en-atributos">Búsqueda de
vértices y aristas basada en atributos</a>
<ul>
<li><a href="#selecci%C3%B3n-de-v%C3%A9rtices" id="toc-selección-de-vértices">Selección de vértices</a></li>
<li><a href="#selecci%C3%B3n-de-aristas" id="toc-selección-de-aristas">Selección de aristas</a></li>
</ul></li>
<li><a href="#tratar-un-grafo-como-una-matriz-de-adyacencia" id="toc-tratar-un-grafo-como-una-matriz-de-adyacencia">Tratar un grafo
como una matriz de adyacencia</a></li>
<li><a href="#dise%C3%B1os-y-graficaci%C3%B3n" id="toc-diseños-y-graficación">Diseños y graficación</a>
<ul>
<li><a href="#algoritmos-de-dise%C3%B1o" id="toc-algoritmos-de-diseño">Algoritmos de diseño</a></li>
<li><a href="#dibujar-un-grafo-utilizando-un-dise%C3%B1o" id="toc-dibujar-un-grafo-utilizando-un-diseño">Dibujar un grafo
utilizando un diseño</a></li>
<li><a href="#atributos-de-los-v%C3%A9rtices-para-graficar" id="toc-atributos-de-los-vértices-para-graficar">Atributos de los
vértices para graficar</a></li>
<li><a href="#atributos-de-las-aristas-para-graficar" id="toc-atributos-de-las-aristas-para-graficar">Atributos de las aristas
para graficar</a></li>
<li><a href="#argumentos-m%C3%A1s-comunes-de-plot" id="toc-argumentos-más-comunes-de-plot">Argumentos más comunes de
<code>plot()</code></a></li>
</ul></li>
<li><a href="#igraph-y-el-mundo-exterior" id="toc-igraph-y-el-mundo-exterior">igraph y el mundo exterior</a></li>
<li><a href="#d%C3%B3nde-ir-a-continuaci%C3%B3n" id="toc-dónde-ir-a-continuación">Dónde ir a continuación</a></li>
<li><a href="#informaci%C3%B3n-de-la-sesi%C3%B3n" id="toc-información-de-la-sesión">Información de la sesión</a></li>
</ul>
</div>

<p><code>igraph</code> es una biblioteca rápida y de código abierto para
el análisis de grafos o redes. El núcleo de ésta libreria se encuentra
escrito en C y contiene enlaces para lenguajes de alto nivel como <a href="https://r.igraph.org/">R</a>, <a href="https://python.igraph.org/">Python</a>, y <a href="http://szhorvat.net/pelican/igraphm-a-mathematica-interface-for-igraph.html">Mathematica</a>.
Esta viñeta pretende darte una visión general de las funciones
disponibles de <code>igraph</code> en R. Para obtener información
detallada de cada función, consulta <a href="https://r.igraph.org/reference/" class="uri">https://r.igraph.org/reference/</a>.</p>
<hr />
<p><strong>NOTA:</strong> A lo largo de este tutorial, utilizaremos las
palabras <code>grafo</code> y <code>red</code> como sinónimos, y también
<code>vértice</code> o <code>nodo</code> como sinónimos.</p>
<hr />
<div id="instalación" class="section level2">
<h2>Instalación</h2>
<p>Para instalar la librería desde CRAN, usa:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">&quot;igraph&quot;</span>)</span></code></pre></div>
<p>Encontrarás más información sobre dependencias, requisitos y
resolución de problemas sobre la instalación en la <a href="https://r.igraph.org/">página principal</a>.</p>
</div>
<div id="uso-de-igraph" class="section level2">
<h2>Uso de igraph</h2>
<p>Para utilizar <code>igraph</code> en tu código de R, primero debes
cargar la biblioteca:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;igraph&quot;</span>)</span></code></pre></div>
<p>Ahora tienes todas las funciones de <code>igraph</code>
disponibles.</p>
</div>
<div id="crear-un-grafo" class="section level2">
<h2>Crear un grafo</h2>
<p><code>igraph</code> ofrece muchas formas de crear un grafo. La más
sencilla es con la función <code>make_empty_graph()</code>:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_empty_graph</span>()</span></code></pre></div>
<p>La forma más común de crear un grafo es con
<code>make_graph()</code>, que construye un grafo basado en especificar
las aristas. Por ejemplo, Para hacer un grafo con 10 nodos (numerados
<code>1</code> a <code>10</code>) y dos aristas que conecten los nodos
<code>1-2</code> y <code>1-5</code>:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(<span class="at">edges =</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>, <span class="dv">1</span>,<span class="dv">5</span>), <span class="at">n=</span><span class="dv">10</span>, <span class="at">directed =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<p>A partir de igraph 0.8.0, también puedes incluir literales mediante
la notación de fórmulas de igraph. En este caso, el primer término de la
fórmula tiene que empezar con un carácter <code>~</code>, como
comúnmente se usa en las fórmulas en R. Las expresiones constan de los
nombres de los vértices y los operadores de las aristas. El operador de
un arista es una secuencia de caracteres <code>-</code> y
<code>+</code>, el primero es para indicar propiamente las aristas y el
segundo para las puntas de flecha (dirección). Puedes utilizar tantos
caracteres <code>-</code> como quieras para “dibujarlas”. Si todos los
operadores de un arista están formados únicamente por caracteres
<code>-</code>, el grafo será no dirigido, mientras que un único
carácter <code>+</code> implica un grafo dirigido. Por ejemplo, para
crear el mismo grafo que antes:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(<span class="sc">~</span> <span class="dv">1</span><span class="sc">--</span><span class="dv">2</span>, <span class="dv">1</span><span class="sc">--</span><span class="dv">5</span>, <span class="dv">3</span>, <span class="dv">4</span>, <span class="dv">5</span>, <span class="dv">6</span>, <span class="dv">7</span>, <span class="dv">8</span>, <span class="dv">9</span>, <span class="dv">10</span>)</span></code></pre></div>
<p>Podemos imprimir el grafo para obtener un resumen de sus nodos y
aristas:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>g</span></code></pre></div>
<pre><code>## IGRAPH e2af91f UN-- 10 2 -- 
## + attr: name (v/c)
## + edges from e2af91f (vertex names):
## [1] 1--2 1--5</code></pre>
<p>Esto significa: grafo no dirigido (<strong>U</strong>ndirected) con
<strong>10</strong> vértices y <strong>2</strong> aristas, que se
enlistan en la última parte. Si el grafo tiene un atributo [nombre],
también se imprime.</p>
<hr />
<p><strong>NOTA</strong>: <code>summary()</code> no enlista las aristas,
lo cual es conveniente para grafos grandes con millones de aristas:</p>
<hr />
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div>
<pre><code>## IGRAPH e2af91f UN-- 10 2 -- 
## + attr: name (v/c)</code></pre>
<p>También <code>make_graph()</code> puede crear algunos grafos
destacados con sólo especificar su nombre. Por ejemplo, puedes generar
el grafo que muestra la red social del club de kárate de Zachary, que
refleja la amistad entre los 34 miembros del club de una universidad de
los Estados Unidos en la década de los 70s:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(<span class="st">&quot;Zachary&quot;</span>)</span></code></pre></div>
<p>Para observar un grafo puedes utilizar <code>plot()</code>:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="fu">plot</span>(g)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>Más adelante en este tutorial se ofrece una descripción detallada de
las opciones para graficar un grafo.</p>
</div>
<div id="ids-de-vértices-y-aristas" class="section level2">
<h2>IDs de vértices y aristas</h2>
<p>Los vértices y las aristas tienen un identificador numérico en
igraph. Los ID de los vértices son siempre consecutivos y empiezan por
1. Para un grafo con “n” vértices, los ID de los vértices están siempre
entre 1 y “n”. Si alguna operación cambia el número de vértices en los
grafos, por ejemplo, se crea un subgrafo mediante
<code>induced_subgraph()</code>, entonces los vértices se vuelven a
enumerar para satisfacer este criterio.</p>
<p>Lo mismo ocurre con las aristas: los ID de las aristas están siempre
entre 1 y “m”, el número total de aristas del grafo.</p>
<hr />
<p><strong>NOTA</strong>: Si estás familiarizado con C o con la interfaz
<a href="https://python.igraph.org/en/stable/">Python</a> de
<code>igraph</code>, te habrás dado cuenta de que en esos lenguajes los
IDs de vértices y aristas empiezan por 0. En la interfaz de R, ambos
empiezan por 1, para mantener la coherencia con la convención de cada
lenguaje.</p>
<hr />
<p>Además de los IDs, a los vértices y aristas se les puede asignar un
nombre y otros atributos. Esto facilita su seguimiento cada vez que se
altera un grafo. Más adelante en este tutorial se muestran ejemplos de
cómo alterar estas características.</p>
</div>
<div id="añadir-y-borrar-vértices-y-aristas" class="section level2">
<h2>Añadir y borrar vértices y aristas</h2>
<p>Sigamos trabajando con el grafo del club de kárate. Para añadir uno o
más vértices a un grafo existente, utiliza
<code>add_vertices()</code>:</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">add_vertices</span>(g, <span class="dv">3</span>)</span></code></pre></div>
<p>Del mismo modo, para añadir aristas puedes utilizar
<code>add_edges()</code>:</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">add_edges</span>(g, <span class="at">edges =</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">35</span>, <span class="dv">1</span>,<span class="dv">36</span>, <span class="dv">34</span>,<span class="dv">37</span>))</span></code></pre></div>
<p>Las aristas se añaden especificando el ID del vértice origen y el
vértice destino de cada arista. Con las instrucciones anteriores se
añaden tres aristas, una que conecta los vértices <code>1</code> y
<code>35</code>, otra que conecta los vértices <code>1</code> y
<code>36</code> y otra que conecta los vértices <code>34</code> y
<code>37</code>.</p>
<p>Además de las funciones <code>add_vertices()</code> y
<code>add_edges()</code>, se puede utilizar el operador “+” para añadir
vértices o aristas al grafo. La operación que se realice dependerá del
tipo de argumento del lado derecho:</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> g <span class="sc">+</span> <span class="fu">edges</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">35</span>, <span class="dv">1</span>,<span class="dv">36</span>, <span class="dv">34</span>,<span class="dv">37</span>))</span></code></pre></div>
<p>Puedes añadir un solo vértice/arista a la vez usando
<code>add_vertex()</code> y <code>add_edge()</code> (singular).</p>
<p><strong>Advertencia</strong>: Si necesitas añadir múltiples aristas a
un grafo, es mucho más eficiente usar <code>add_edges()</code> una vez
que utilizar repetidamente <code>add_edge()</code> con una nueva arista
a la vez. Lo mismo ocurre al eliminar aristas y vértices.</p>
<p>Si intentas añadir aristas a vértices con IDs no válidos (por
ejemplo, intentas añadir una arista al vértice <code>38</code> cuando el
grafo sólo tiene 37 vértices), <code>igraph</code> muestra un error:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">add_edges</span>(g, <span class="at">edges =</span> <span class="fu">c</span>(<span class="dv">38</span>, <span class="dv">37</span>))</span></code></pre></div>
<pre><code>## Error in `add_edges()`:
## ! At vendor/cigraph/src/graph/type_indexededgelist.c:261 : Out-of-range vertex IDs when adding edges. Invalid vertex ID</code></pre>
<p>Añadamos más vértices y aristas a nuestro grafo. En
<code>igraph</code> podemos utilizar el paquete <code>magrittr</code>,
que proporciona un mecanismo para encadenar comandos con el operador
<code>%&gt;%</code>:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> g <span class="sc">%&gt;%</span> </span>
<span id="cb17-2"><a href="#cb17-2" tabindex="-1"></a>  <span class="fu">add_edges</span>(<span class="at">edges =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">34</span>)) <span class="sc">%&gt;%</span> </span>
<span id="cb17-3"><a href="#cb17-3" tabindex="-1"></a>  <span class="fu">add_vertices</span>(<span class="dv">3</span>) <span class="sc">%&gt;%</span></span>
<span id="cb17-4"><a href="#cb17-4" tabindex="-1"></a>  <span class="fu">add_edges</span>(<span class="at">edges =</span> <span class="fu">c</span>(<span class="dv">38</span>, <span class="dv">39</span>, <span class="dv">39</span>, <span class="dv">40</span>, <span class="dv">40</span>, <span class="dv">38</span>, <span class="dv">40</span>, <span class="dv">37</span>))</span>
<span id="cb17-5"><a href="#cb17-5" tabindex="-1"></a>g</span></code></pre></div>
<pre><code>## IGRAPH 3b9cd35 U--- 40 86 -- Zachary
## + attr: name (g/c)
## + edges from 3b9cd35:
##  [1]  1-- 2  1-- 3  1-- 4  1-- 5  1-- 6  1-- 7  1-- 8  1-- 9  1--11  1--12
## [11]  1--13  1--14  1--18  1--20  1--22  1--32  2-- 3  2-- 4  2-- 8  2--14
## [21]  2--18  2--20  2--22  2--31  3-- 4  3-- 8  3--28  3--29  3--33  3--10
## [31]  3-- 9  3--14  4-- 8  4--13  4--14  5-- 7  5--11  6-- 7  6--11  6--17
## [41]  7--17  9--31  9--33  9--34 10--34 14--34 15--33 15--34 16--33 16--34
## [51] 19--33 19--34 20--34 21--33 21--34 23--33 23--34 24--26 24--28 24--33
## [61] 24--34 24--30 25--26 25--28 25--32 26--32 27--30 27--34 28--34 29--32
## [71] 29--34 30--33 30--34 31--33 31--34 32--33 32--34 33--34  1--35  1--36
## + ... omitted several edges</code></pre>
<p>Ahora tenemos un grafo no dirigido con 40 vértices y 89 aristas. Los
IDs de los vértices y aristas son siempre <em>contiguos</em>, así que si
borras un vértice, todos los vértices subsecuentes se vuelven a
enumerar. Cuando se re-numera un vértice, las aristas
<strong>no</strong> se vuelven a enumerar, pero sí sus vértices origen y
destino. Puedes usar <code>delete_vertices()</code> y
<code>delete_edges()</code> para realizar estas operaciones. Por
ejemplo, para borrar la arista que conecta los vértices
<code>1-34</code>, obtén su ID y luego bórrala:</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a>edge_id_para_borrar <span class="ot">&lt;-</span> <span class="fu">get_edge_ids</span>(g, <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">34</span>))</span>
<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a>edge_id_para_borrar</span></code></pre></div>
<pre><code>## [1] 82</code></pre>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">delete_edges</span>(g, edge_id_para_borrar)</span></code></pre></div>
<p>Por ejemplo, para crear un grafo con forma de anillo y para
partirlo:</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_ring</span>(<span class="dv">10</span>) <span class="sc">%&gt;%</span> <span class="fu">delete_edges</span>(<span class="st">&quot;10|1&quot;</span>)</span>
<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a><span class="fu">plot</span>(g)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>El ejemplo anterior muestra que también puedes referirte a las
aristas indicando los IDs de los vértices origen y destino, conectados
por el símbolo <code>|</code>. En el ejemplo, <code>&quot;10|1&quot;</code>
significa la arista que conecta el vértice <code>10</code> con el
vértice <code>1</code>. Por supuesto, también puedes usar los IDs de las
aristas directamente, o recuperarlos con la función
<code>get_edge_ids()</code>:</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_ring</span>(<span class="dv">5</span>)</span>
<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">delete_edges</span>(g, <span class="fu">get_edge_ids</span>(g, <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">5</span>, <span class="dv">4</span>,<span class="dv">5</span>)))</span>
<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a><span class="fu">plot</span>(g)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>Veamos otro ejemplo, hagamos un grafo cordal. Recuerda que un grafo
es cordal (o triangulado) si cada uno de sus ciclos de cuatro o más
nodos tienen una “cuerda”, que es una arista que une dos nodos que no
son adyacentes en el ciclo. En primer lugar, vamos a crear el grafo
inicial utilizando <code>graph_from_literal()</code>:</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a>g1 <span class="ot">&lt;-</span> <span class="fu">graph_from_literal</span>(</span>
<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a>  A<span class="sc">-</span>B<span class="sc">:</span>C<span class="sc">:</span>I, </span>
<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a>  B<span class="sc">-</span>A<span class="sc">:</span>C<span class="sc">:</span>D, </span>
<span id="cb24-4"><a href="#cb24-4" tabindex="-1"></a>  C<span class="sc">-</span>A<span class="sc">:</span>B<span class="sc">:</span>E<span class="sc">:</span>H, </span>
<span id="cb24-5"><a href="#cb24-5" tabindex="-1"></a>  D<span class="sc">-</span>B<span class="sc">:</span>E<span class="sc">:</span>F,</span>
<span id="cb24-6"><a href="#cb24-6" tabindex="-1"></a>  E<span class="sc">-</span>C<span class="sc">:</span>D<span class="sc">:</span>F<span class="sc">:</span>H, </span>
<span id="cb24-7"><a href="#cb24-7" tabindex="-1"></a>  F<span class="sc">-</span>D<span class="sc">:</span>E<span class="sc">:</span>G, </span>
<span id="cb24-8"><a href="#cb24-8" tabindex="-1"></a>  G<span class="sc">-</span>F<span class="sc">:</span>H, </span>
<span id="cb24-9"><a href="#cb24-9" tabindex="-1"></a>  H<span class="sc">-</span>C<span class="sc">:</span>E<span class="sc">:</span>G<span class="sc">:</span>I,</span>
<span id="cb24-10"><a href="#cb24-10" tabindex="-1"></a>  I<span class="sc">-</span>A<span class="sc">:</span>H</span>
<span id="cb24-11"><a href="#cb24-11" tabindex="-1"></a>)</span>
<span id="cb24-12"><a href="#cb24-12" tabindex="-1"></a><span class="fu">plot</span>(g1)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>En este ejemplo, se ha utilizado el operador <code>:</code> para
definir conjuntos de vértices. Si el operador de un arista conecta dos
conjuntos de vértices, entonces cada vértice del primer conjunto estará
conectado a cada vértice del segundo conjunto. A continuación utilizamos
<code>is_chordal()</code> para evaluar si nuestro grafo es cordal y
buscar qué aristas faltan para rellenar el grafo:</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="fu">is_chordal</span>(g1, <span class="at">fillin=</span><span class="cn">TRUE</span>)</span></code></pre></div>
<pre><code>## $chordal
## [1] FALSE
## 
## $fillin
##  [1] 2 6 8 7 5 7 2 7 6 1 7 1
## 
## $newgraph
## NULL</code></pre>
<p>Luego, en una sola línea podemos añadir las aristas necesarias para
que el grafo inicial sea cordal:</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a>chordal_graph <span class="ot">&lt;-</span> <span class="fu">add_edges</span>(g1, <span class="fu">is_chordal</span>(g1, <span class="at">fillin=</span><span class="cn">TRUE</span>)<span class="sc">$</span>fillin)</span>
<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a><span class="fu">plot</span>(chordal_graph)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="construcción-de-grafos" class="section level2">
<h2>Construcción de grafos</h2>
<p>Además de <code>make_empty_graph()</code>, <code>make_graph()</code>
y <code>make_graph_from_literal()</code>, <code>igraph</code> incluye
muchas otras funciones para construir un grafo. Algunas son
<em>deterministas</em>, es decir, producen el mismo grafo cada vez, por
ejemplo <code>make_tree()</code>:</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a>graph1 <span class="ot">&lt;-</span> <span class="fu">make_tree</span>(<span class="dv">127</span>, <span class="dv">2</span>, <span class="at">mode =</span> <span class="st">&quot;undirected&quot;</span>)</span>
<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div>
<pre><code>## IGRAPH e4ec646 U--- 5 3 -- Ring graph
## + attr: name (g/c), mutual (g/l), circular (g/l)</code></pre>
<p>Esto genera un grafo regular en forma de árbol con 127 vértices, cada
vértice con dos hijos. No importa cuántas veces llames a
<code>make_tree()</code>, el grafo generado será siempre el mismo si
utilizas los mismos parámetros:</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a>graph2 <span class="ot">&lt;-</span> <span class="fu">make_tree</span>(<span class="dv">127</span>, <span class="dv">2</span>, <span class="at">mode =</span> <span class="st">&quot;undirected&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" tabindex="-1"></a><span class="fu">identical_graphs</span>(graph1, graph2)</span></code></pre></div>
<pre><code>## [1] TRUE</code></pre>
<p>Otras funciones son <em>estocásticas</em>, lo cual quiere decir que
producen un grafo diferente cada vez; por ejemplo,
<code>sample_grg()</code>:</p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" tabindex="-1"></a>graph1 <span class="ot">&lt;-</span> <span class="fu">sample_grg</span>(<span class="dv">100</span>, <span class="fl">0.2</span>)</span>
<span id="cb33-2"><a href="#cb33-2" tabindex="-1"></a><span class="fu">summary</span>(graph1)</span></code></pre></div>
<pre><code>## IGRAPH a38eb25 U--- 100 628 -- Geometric random graph
## + attr: name (g/c), radius (g/n), torus (g/l)</code></pre>
<p>Esto genera un grafo geométrico aleatorio: Se eligen <em>n</em>
puntos de forma aleatoria y uniforme dentro del espacio métrico, y los
pares de puntos más cercanos entre sí respecto a una distancia
predeterminada <em>d</em> se conectan mediante una arista. Si se generan
GRGs con los mismos parámetros, serán diferentes:</p>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" tabindex="-1"></a>graph2 <span class="ot">&lt;-</span> <span class="fu">sample_grg</span>(<span class="dv">100</span>, <span class="fl">0.2</span>)</span>
<span id="cb35-2"><a href="#cb35-2" tabindex="-1"></a><span class="fu">identical_graphs</span>(graph1, graph2)</span></code></pre></div>
<pre><code>## [1] FALSE</code></pre>
<p>Una forma un poco más relajada de comprobar si los grafos son
equivalentes es mediante <code>isomorphic()</code>. Se dice que dos
grafos son isomorfos si tienen el mismo número de componentes (vértices
y aristas) y mantienen una correspondencia uno a uno entre vértices y
aristas, es decir, están conectados de la misma manera:</p>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" tabindex="-1"></a><span class="fu">isomorphic</span>(graph1, graph2)</span></code></pre></div>
<pre><code>## [1] FALSE</code></pre>
<p>Comprobar el isomorfismo puede llevar un tiempo en el caso de grafos
grandes (en este caso, la respuesta puede darse rápidamente comprobando
la secuencia de grados de los dos grafos).
<code>identical_graph()</code> es un criterio más estricto que
<code>isomorphic()</code>: los dos grafos deben tener la misma lista de
vértices y aristas, exactamente en el mismo orden, con la misma
direccionalidad, y los dos grafos también deben tener idénticos
atributos de grafo, vértice y arista.</p>
</div>
<div id="establecer-y-recuperar-atributos" class="section level2">
<h2>Establecer y recuperar atributos</h2>
<p>Además de los IDs, los vértices y aristas pueden tener
<em>atributos</em> como un nombre, coordenadas para graficar, metadatos
y pesos. El propio grafo también puede tener estos atributos (por
ejemplo, un nombre, que se mostrará en <code>summary</code>). En cierto
sentido, cada grafo, vértice y arista puede ser utilizado como un
espacio de nombres en R para almacenar y recuperar estos atributos.</p>
<p>Para demostrar el uso de los atributos, creemos una red social
sencilla:</p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(</span>
<span id="cb39-2"><a href="#cb39-2" tabindex="-1"></a>  <span class="sc">~</span> Alice<span class="sc">-</span>Boris<span class="sc">:</span>Himari<span class="sc">:</span>Moshe,</span>
<span id="cb39-3"><a href="#cb39-3" tabindex="-1"></a>  Himari<span class="sc">-</span>Alice<span class="sc">:</span>Nang<span class="sc">:</span>Moshe<span class="sc">:</span>Samira,</span>
<span id="cb39-4"><a href="#cb39-4" tabindex="-1"></a>  Ibrahim<span class="sc">-</span>Nang<span class="sc">:</span>Moshe, </span>
<span id="cb39-5"><a href="#cb39-5" tabindex="-1"></a>  Nang<span class="sc">-</span>Samira</span>
<span id="cb39-6"><a href="#cb39-6" tabindex="-1"></a>)</span></code></pre></div>
<p>Cada vértice representa a una persona, por lo que queremos almacenar
sus edades, géneros y el tipo de conexión entre dos personas
(<code>is_formal()</code> se refiere a si una conexión entre una persona
y otra es formal o informal, es decir, colegas o amigos). El operador
<code>$</code> es un atajo para obtener y establecer atributos de un
grafo. Es más corto y tan legible como <code>graph_attr()</code> y
<code>set_graph_attr()</code>.</p>
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>age <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">25</span>, <span class="dv">31</span>, <span class="dv">18</span>, <span class="dv">23</span>, <span class="dv">47</span>, <span class="dv">22</span>, <span class="dv">50</span>) </span>
<span id="cb40-2"><a href="#cb40-2" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>)</span>
<span id="cb40-3"><a href="#cb40-3" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="cn">FALSE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">TRUE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">FALSE</span>)</span>
<span id="cb40-4"><a href="#cb40-4" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div>
<pre><code>## IGRAPH 75370ad UN-- 7 9 -- 
## + attr: name (v/c), age (v/n), gender (v/c), is_formal (e/l)</code></pre>
<p><code>V</code> y <code>E</code> son la forma estándar de obtener una
secuencia de todos los vértices y aristas respectivamente. Esto asigna
un atributo a <em>todos</em> los vértices/aristas a la vez. Otra forma
de generar nuestra red social es con el uso de
<code>set_vertex_attr()</code> y <code>set_edge_attr()</code> y el
operador <code>%&gt;%</code>:</p>
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(</span>
<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a>  <span class="sc">~</span> Alice<span class="sc">-</span>Boris<span class="sc">:</span>Himari<span class="sc">:</span>Moshe, </span>
<span id="cb42-3"><a href="#cb42-3" tabindex="-1"></a>  Himari<span class="sc">-</span>Alice<span class="sc">:</span>Nang<span class="sc">:</span>Moshe<span class="sc">:</span>Samira,</span>
<span id="cb42-4"><a href="#cb42-4" tabindex="-1"></a>  Ibrahim<span class="sc">-</span>Nang<span class="sc">:</span>Moshe, </span>
<span id="cb42-5"><a href="#cb42-5" tabindex="-1"></a>  Nang<span class="sc">-</span>Samira</span>
<span id="cb42-6"><a href="#cb42-6" tabindex="-1"></a>) <span class="sc">%&gt;%</span></span>
<span id="cb42-7"><a href="#cb42-7" tabindex="-1"></a>  <span class="fu">set_vertex_attr</span>(<span class="st">&quot;age&quot;</span>, <span class="at">value =</span> <span class="fu">c</span>(<span class="dv">25</span>, <span class="dv">31</span>, <span class="dv">18</span>, <span class="dv">23</span>, <span class="dv">47</span>, <span class="dv">22</span>, <span class="dv">50</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb42-8"><a href="#cb42-8" tabindex="-1"></a>  <span class="fu">set_vertex_attr</span>(<span class="st">&quot;gender&quot;</span>, <span class="at">value =</span> <span class="fu">c</span>(<span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb42-9"><a href="#cb42-9" tabindex="-1"></a>  <span class="fu">set_edge_attr</span>(<span class="st">&quot;is_formal&quot;</span>, <span class="at">value =</span> <span class="fu">c</span>(<span class="cn">FALSE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">TRUE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">FALSE</span>))</span>
<span id="cb42-10"><a href="#cb42-10" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div>
<p>Para asignar o modificar un atributo a un único vértice/arista:</p>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal</span></code></pre></div>
<pre><code>## [1] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE</code></pre>
<div class="sourceCode" id="cb45"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal[<span class="dv">1</span>] <span class="ot">&lt;-</span> <span class="cn">TRUE</span></span>
<span id="cb45-2"><a href="#cb45-2" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal</span></code></pre></div>
<pre><code>## [1]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE</code></pre>
<p>Los valores de los atributos pueden establecerse en cualquier objeto
de R, pero ten en cuenta que almacenar el grafo en algunos formatos
puede provocar la pérdida de valores en atributos complejos. Los
vértices, las aristas y el propio grafo pueden utilizarse para
establecer atributos, por ejemplo, para añadir una fecha al grafo:</p>
<div class="sourceCode" id="cb47"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" tabindex="-1"></a>g<span class="sc">$</span>date <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;2022-02-11&quot;</span>)</span>
<span id="cb47-2"><a href="#cb47-2" tabindex="-1"></a><span class="fu">graph_attr</span>(g, <span class="st">&quot;date&quot;</span>)</span></code></pre></div>
<pre><code>## [1] &quot;2022-02-11&quot;</code></pre>
<p>Para recuperar atributos, también puedes utilizar
<code>graph_attr()</code>, <code>vertex_attr()</code> y
<code>edge_attr()</code>. Para encontrar el ID de un vértice puedes
utilizar la función <code>match()</code>:</p>
<div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" tabindex="-1"></a><span class="fu">match</span>(<span class="fu">c</span>(<span class="st">&quot;Ibrahim&quot;</span>), <span class="fu">V</span>(g)<span class="sc">$</span>name)</span></code></pre></div>
<pre><code>## [1] 7</code></pre>
<p>Para asignar atributos a un subconjunto de vértices o aristas, puedes
utilizar:</p>
<div class="sourceCode" id="cb51"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>name[<span class="dv">1</span><span class="sc">:</span><span class="dv">3</span>] <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Alejandra&quot;</span>, <span class="st">&quot;Bruno&quot;</span>, <span class="st">&quot;Carmina&quot;</span>)</span>
<span id="cb51-2"><a href="#cb51-2" tabindex="-1"></a><span class="fu">V</span>(g)</span></code></pre></div>
<pre><code>## + 7/7 vertices, named, from 75370ad:
## [1] Alejandra Bruno     Carmina   Moshe     Nang      Samira    Ibrahim</code></pre>
<p>Para eliminar atributos:</p>
<div class="sourceCode" id="cb53"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">delete_vertex_attr</span>(g, <span class="st">&quot;gender&quot;</span>)</span>
<span id="cb53-2"><a href="#cb53-2" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>gender</span></code></pre></div>
<pre><code>## NULL</code></pre>
<p>Si quieres guardar un grafo en R con todos los atributos utiliza la
función <code>saveRDS()</code> (y <code>readRDS()</code> para
recuperarlo más tarde).</p>
</div>
<div id="propiedades-estructurales-de-los-grafos" class="section level2">
<h2>Propiedades estructurales de los grafos</h2>
<p>igraph proporciona un amplio conjunto de métodos para calcular varias
propiedades estructurales de los grafos. Está más allá del alcance de
este tutorial documentar todos ellos, por lo que esta sección sólo
presentará algunos de ellos con fines ilustrativos. Trabajaremos con la
pequeña red social que construimos en la sección anterior.</p>
<p>Probablemente, la propiedad más sencilla en la que se puede pensar es
el “grado del vértice”. El grado de un vértice es igual al número de
aristas incidentes a él. En el caso de los grafos dirigidos, también
podemos definir el <code>grado de entrada</code> (el número de aristas
que apuntan hacia el vértice) y el <code>grado de salida</code> (el
número de aristas que se originan en el vértice). igraph es capaz de
calcularlos todos utilizando una sintaxis sencilla:</p>
<div class="sourceCode" id="cb55"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" tabindex="-1"></a><span class="fu">degree</span>(g)</span></code></pre></div>
<pre><code>## Alejandra     Bruno   Carmina     Moshe      Nang    Samira   Ibrahim 
##         3         1         4         3         3         2         2</code></pre>
<p>Si el grafo fuera dirigido, podríamos calcular los grados de entrada
y salida por separado utilizando <code>degree(mode = &quot;in&quot;)</code> y
<code>degree(mode = &quot;out&quot;)</code>. También puedes pasar un único ID de
un vértice o una lista de IDs de los vértices a <code>degree()</code> si
quieres calcular los grados sólo para un subconjunto de vértices:</p>
<div class="sourceCode" id="cb57"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb57-1"><a href="#cb57-1" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="dv">7</span>)</span></code></pre></div>
<pre><code>## Ibrahim 
##       2</code></pre>
<div class="sourceCode" id="cb59"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb59-1"><a href="#cb59-1" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="at">v =</span> <span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">4</span>,<span class="dv">5</span>))</span></code></pre></div>
<pre><code>## Carmina   Moshe    Nang 
##       4       3       3</code></pre>
<p>La mayoría de las funciones que aceptan los IDs de los vértices
también aceptan los “nombres” de los vértices (es decir, los valores del
atributo <code>name</code> del vértice) siempre que los nombres sean
únicos:</p>
<div class="sourceCode" id="cb61"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb61-1"><a href="#cb61-1" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="at">v =</span> <span class="fu">c</span>(<span class="st">&quot;Carmina&quot;</span>, <span class="st">&quot;Moshe&quot;</span>, <span class="st">&quot;Nang&quot;</span>))</span></code></pre></div>
<pre><code>## Carmina   Moshe    Nang 
##       4       3       3</code></pre>
<p>También funciona para vértices individuales:</p>
<div class="sourceCode" id="cb63"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="st">&quot;Bruno&quot;</span>)</span></code></pre></div>
<pre><code>## Bruno 
##     1</code></pre>
<p>De igual manera, se utiliza una sintaxis similar para la mayoría de
las propiedades estructurales que igraph puede calcular. Para las
propiedades de los vértices, las funciones aceptan un ID, un nombre o
una lista de IDs o nombres (y si se omiten, el valor predeterminado es
el conjunto de todos los vértices). Para las propiedades de aristas, las
funciones aceptan un único ID o una lista de IDs.</p>
<hr />
<p><strong>NOTA:</strong> Para algunas mediciones, no tiene sentido
calcularlas sólo para unos pocos vértices o aristas en lugar de para
todo el grafo, ya que de todas formas llevaría el mismo tiempo. En este
caso, las funciones no aceptan IDs de vértices o aristas, pero se puede
restringir la lista resultante utilizando operaciones estándar. Un
ejemplo es la centralidad de vectores propios
(<code>evcent()</code>).</p>
<hr />
<p>Además del grado, igraph incluye funciones integradas para calcular
muchas otras propiedades de centralidad, como la intermediación de
vértices y aristas (<code>edge_betweenness()</code>) o el PageRank de
Google (<code>page_rank()</code>), por nombrar algunas. Aquí sólo
ilustraremos la intermediación de aristas:</p>
<div class="sourceCode" id="cb65"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb65-1"><a href="#cb65-1" tabindex="-1"></a><span class="fu">edge_betweenness</span>(g)</span></code></pre></div>
<pre><code>## [1] 6 6 4 3 4 4 4 2 3</code></pre>
<p>De este modo, ahora también podemos averiguar qué conexiones tienen
la mayor centralidad de intermediación:</p>
<div class="sourceCode" id="cb67"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb67-1"><a href="#cb67-1" tabindex="-1"></a>ebs <span class="ot">&lt;-</span> <span class="fu">edge_betweenness</span>(g)</span>
<span id="cb67-2"><a href="#cb67-2" tabindex="-1"></a><span class="fu">as_edgelist</span>(g)[ebs <span class="sc">==</span> <span class="fu">max</span>(ebs), ]</span></code></pre></div>
<pre><code>##      [,1]        [,2]     
## [1,] &quot;Alejandra&quot; &quot;Bruno&quot;  
## [2,] &quot;Alejandra&quot; &quot;Carmina&quot;</code></pre>
</div>
<div id="búsqueda-de-vértices-y-aristas-basada-en-atributos" class="section level2">
<h2>Búsqueda de vértices y aristas basada en atributos</h2>
<div id="selección-de-vértices" class="section level3">
<h3>Selección de vértices</h3>
<p>Tomando como ejemplo la red social anteriormente creada, te gustaría
averiguar quién tiene el mayor grado. Puedes hacerlo con las
herramientas presentadas hasta ahora y con la función
<code>which.max()</code>:</p>
<div class="sourceCode" id="cb69"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb69-1"><a href="#cb69-1" tabindex="-1"></a><span class="fu">which.max</span>(<span class="fu">degree</span>(g))</span></code></pre></div>
<pre><code>## Carmina 
##       3</code></pre>
<p>Otro ejemplo sería seleccionar sólo los vértices que tienen IDs
impares, utilizando la función <code>V()</code>:</p>
<div class="sourceCode" id="cb71"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb71-1"><a href="#cb71-1" tabindex="-1"></a>graph <span class="ot">&lt;-</span> <span class="fu">graph.full</span>(<span class="at">n=</span><span class="dv">10</span>)</span>
<span id="cb71-2"><a href="#cb71-2" tabindex="-1"></a>only_odd_vertices <span class="ot">&lt;-</span> <span class="fu">which</span>(<span class="fu">V</span>(graph)<span class="sc">%%</span><span class="dv">2</span><span class="sc">==</span><span class="dv">1</span>)</span>
<span id="cb71-3"><a href="#cb71-3" tabindex="-1"></a><span class="fu">length</span>(only_odd_vertices)</span></code></pre></div>
<pre><code>## [1] 5</code></pre>
<p>Por supuesto, es posible seleccionar vértices o aristas mediante
índices posicionales:</p>
<div class="sourceCode" id="cb73"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb73-1"><a href="#cb73-1" tabindex="-1"></a>seq <span class="ot">&lt;-</span> <span class="fu">V</span>(graph)[<span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">7</span>]</span>
<span id="cb73-2"><a href="#cb73-2" tabindex="-1"></a>seq</span></code></pre></div>
<pre><code>## + 3/10 vertices, from 47d75e7:
## [1] 2 3 7</code></pre>
<div class="sourceCode" id="cb75"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb75-1"><a href="#cb75-1" tabindex="-1"></a>seq <span class="ot">&lt;-</span> seq[<span class="dv">1</span>, <span class="dv">3</span>]    <span class="co"># filtrar un conjunto de vértices existente</span></span>
<span id="cb75-2"><a href="#cb75-2" tabindex="-1"></a>seq</span></code></pre></div>
<pre><code>## + 2/10 vertices, from 47d75e7:
## [1] 2 7</code></pre>
<p>Al seleccionar un vértice que no existe se produce un error:</p>
<div class="sourceCode" id="cb77"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb77-1"><a href="#cb77-1" tabindex="-1"></a>seq <span class="ot">&lt;-</span> <span class="fu">V</span>(graph)[<span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="st">&quot;foo&quot;</span>, <span class="fl">3.5</span>]</span></code></pre></div>
<pre><code>## Error in `simple_vs_index()`:
## ! Unknown vertex selected.</code></pre>
<p>Los nombres de los atributos también pueden utilizarse tal cual
dentro de los operadores de indexación (“[]”) de <code>V()</code> y
<code>E()</code>. Esto puede combinarse con la capacidad de R de
utilizar vectores booleanos para indexar y obtener expresiones muy
concisas y legibles para recuperar un subconjunto del set de vértices o
aristas de un grafo. Por ejemplo, el siguiente comando nos da los
nombres de los individuos menores de 30 años de nuestra red social:</p>
<div class="sourceCode" id="cb79"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb79-1"><a href="#cb79-1" tabindex="-1"></a><span class="fu">V</span>(g)[age <span class="sc">&lt;</span> <span class="dv">30</span>]<span class="sc">$</span>name</span></code></pre></div>
<pre><code>## [1] &quot;Alejandra&quot; &quot;Carmina&quot;   &quot;Moshe&quot;     &quot;Samira&quot;</code></pre>
<p>Por supuesto, <code>&lt;</code> no es el único operador booleano que
puede utilizarse para esto. Otras posibilidades son las siguientes:</p>
<table>
<colgroup>
<col width="13%" />
<col width="86%" />
</colgroup>
<thead>
<tr class="header">
<th>Operador</th>
<th>Significado</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>==</code></td>
<td>El valor del atributo/propiedad debe ser <em>igual</em> a</td>
</tr>
<tr class="even">
<td><code>!=</code></td>
<td>El valor del atributo/propiedad debe <em>no ser igual</em> a</td>
</tr>
<tr class="odd">
<td><code>&lt;</code></td>
<td>El valor del atributo/propiedad debe ser <em>menos</em> que</td>
</tr>
<tr class="even">
<td><code>&lt;=</code></td>
<td>El valor del atributo/propiedad debe ser <em>inferior o igual
a</em></td>
</tr>
<tr class="odd">
<td><code>&gt;</code></td>
<td>El valor del atributo/propiedad debe ser <em>mayor que</em></td>
</tr>
<tr class="even">
<td><code>&gt;=</code></td>
<td>El valor del atributo/propiedad debe ser <em>mayor o igual
a</em></td>
</tr>
<tr class="odd">
<td><code>%in%</code></td>
<td>El valor del atributo/propiedad debe estar <em>incluido en</em></td>
</tr>
</tbody>
</table>
<p>También puede crear un operador “no incluido en” a partir de
<code>%in%</code> utilizando el operador <code>Negate</code>:</p>
<div class="sourceCode" id="cb81"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb81-1"><a href="#cb81-1" tabindex="-1"></a><span class="st">`</span><span class="at">%notin%</span><span class="st">`</span> <span class="ot">&lt;-</span> <span class="fu">Negate</span>(<span class="st">`</span><span class="at">%in%</span><span class="st">`</span>)</span></code></pre></div>
<p>Si un atributo tiene el mismo nombre que una función de igraph, debes
tener cuidado ya que la sintaxis puede llegar a ser un poco confusa. Por
ejemplo, si hay un atributo llamado <code>degree</code> que representa
las notas de un examen para cada persona, no debe confundirse con la
función de igraph que calcula los grados de los vértices de una red:</p>
<div class="sourceCode" id="cb82"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb82-1"><a href="#cb82-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>degree <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;A&quot;</span>, <span class="st">&quot;B&quot;</span>, <span class="st">&quot;B+&quot;</span>, <span class="st">&quot;A+&quot;</span>, <span class="st">&quot;C&quot;</span>, <span class="st">&quot;A&quot;</span>, <span class="st">&quot;B&quot;</span>)</span>
<span id="cb82-2"><a href="#cb82-2" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>degree[<span class="fu">degree</span>(g) <span class="sc">==</span> <span class="dv">3</span>]</span></code></pre></div>
<pre><code>## [1] &quot;A&quot;  &quot;A+&quot; &quot;C&quot;</code></pre>
<div class="sourceCode" id="cb84"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb84-1"><a href="#cb84-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>name[<span class="fu">degree</span>(g) <span class="sc">==</span> <span class="dv">3</span>]</span></code></pre></div>
<pre><code>## [1] &quot;Alejandra&quot; &quot;Moshe&quot;     &quot;Nang&quot;</code></pre>
</div>
<div id="selección-de-aristas" class="section level3">
<h3>Selección de aristas</h3>
<p>Las aristas pueden seleccionarse basándose en atributos, igual que
los vértices. Como ya se ha mencionado, la forma estándar de obtener
aristas es <code>E</code>. Además, existen algunas propiedades
estructurales especiales para seleccionar aristas.</p>
<p>El uso de <code>.from()</code> permite filtrar la serie de aristas
desde los vértices de donde proceden. Por ejemplo, para seleccionar
todas las aristas procedentes de Carmina (cuyo ID de vértice es el
3):</p>
<div class="sourceCode" id="cb86"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb86-1"><a href="#cb86-1" tabindex="-1"></a><span class="fu">E</span>(g)[<span class="fu">.from</span>(<span class="dv">3</span>)]</span></code></pre></div>
<pre><code>## + 4/9 edges from 75370ad (vertex names):
## [1] Alejandra--Carmina Carmina  --Moshe   Carmina  --Nang    Carmina  --Samira</code></pre>
<p>Por supuesto, también funciona con nombres de vértices:</p>
<div class="sourceCode" id="cb88"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb88-1"><a href="#cb88-1" tabindex="-1"></a><span class="fu">E</span>(g)[<span class="fu">.from</span>(<span class="st">&quot;Carmina&quot;</span>)]</span></code></pre></div>
<pre><code>## + 4/9 edges from 75370ad (vertex names):
## [1] Alejandra--Carmina Carmina  --Moshe   Carmina  --Nang    Carmina  --Samira</code></pre>
<p>Al usar <code>.to()</code>, se filtran la serie de aristas en función
de los vértices de destino o diana. Esto es diferente de
<code>.from()</code> si el grafo es dirigido, mientras que da la misma
respuesta para grafos no dirigidos. Con <code>.inc()</code> sólo se
seleccionan las aristas que inciden en un único vértice o en al menos
uno de los vértices, independientemente de la dirección de las
aristas.</p>
<p>La expresión <code>%--%</code> es un operador especial que puede
utilizarse para seleccionar todas las aristas entre dos conjuntos de
vértices. Ignora las direcciones de las aristas en los grafos dirigidos.
Por ejemplo, la siguiente expresión selecciona todas las aristas entre
Carmina (su ID de vértice es el 3), Nang (su ID de vértice es el 5) y
Samira (su ID de vértice es el 6):</p>
<div class="sourceCode" id="cb90"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb90-1"><a href="#cb90-1" tabindex="-1"></a><span class="fu">E</span>(g) [ <span class="dv">3</span><span class="sc">:</span><span class="dv">5</span> <span class="sc">%--%</span> <span class="dv">5</span><span class="sc">:</span><span class="dv">6</span> ]</span></code></pre></div>
<pre><code>## + 3/9 edges from 75370ad (vertex names):
## [1] Carmina--Nang   Carmina--Samira Nang   --Samira</code></pre>
<p>Para que el operador <code>%--%</code> funcione con nombres, puedes
construir vectores de caracteres que contengan los nombres y luego
utilizar estos vectores como operandos. Por ejemplo, para seleccionar
todas las aristas que conectan a los hombres con las mujeres, podemos
hacer lo siguiente, luego de volver a añadir el atributo de género que
hemos eliminado anteriormente:</p>
<div class="sourceCode" id="cb92"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb92-1"><a href="#cb92-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb93"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb93-1"><a href="#cb93-1" tabindex="-1"></a>men <span class="ot">&lt;-</span> <span class="fu">V</span>(g)[gender <span class="sc">==</span> <span class="st">&quot;m&quot;</span>]<span class="sc">$</span>name</span>
<span id="cb93-2"><a href="#cb93-2" tabindex="-1"></a>men</span></code></pre></div>
<pre><code>## [1] &quot;Bruno&quot;   &quot;Moshe&quot;   &quot;Nang&quot;    &quot;Ibrahim&quot;</code></pre>
<div class="sourceCode" id="cb95"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb95-1"><a href="#cb95-1" tabindex="-1"></a>women <span class="ot">&lt;-</span> <span class="fu">V</span>(g)[gender <span class="sc">==</span> <span class="st">&quot;f&quot;</span>]<span class="sc">$</span>name</span>
<span id="cb95-2"><a href="#cb95-2" tabindex="-1"></a>women</span></code></pre></div>
<pre><code>## [1] &quot;Alejandra&quot; &quot;Carmina&quot;   &quot;Samira&quot;</code></pre>
<div class="sourceCode" id="cb97"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb97-1"><a href="#cb97-1" tabindex="-1"></a><span class="fu">E</span>(g)[men <span class="sc">%--%</span> women]</span></code></pre></div>
<pre><code>## + 5/9 edges from 75370ad (vertex names):
## [1] Alejandra--Bruno  Alejandra--Moshe  Carmina  --Moshe  Carmina  --Nang  
## [5] Nang     --Samira</code></pre>
</div>
</div>
<div id="tratar-un-grafo-como-una-matriz-de-adyacencia" class="section level2">
<h2>Tratar un grafo como una matriz de adyacencia</h2>
<p>Una matriz de adyacencia es otra manera de representar un grafo. En
la matriz de adyacencia, las filas y columnas están indicadas por los
vértices del grafo y los elementos de la matriz indican el número de
aristas entre los vértices <em>i</em> y <em>j</em>. La matriz de
adyacencia del grafo de nuestra red social imaginaria es:</p>
<div class="sourceCode" id="cb99"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb99-1"><a href="#cb99-1" tabindex="-1"></a><span class="fu">as_adjacency_matrix</span>(g)</span></code></pre></div>
<pre><code>## 7 x 7 sparse Matrix of class &quot;dgCMatrix&quot;
##           Alejandra Bruno Carmina Moshe Nang Samira Ibrahim
## Alejandra         .     1       1     1    .      .       .
## Bruno             1     .       .     .    .      .       .
## Carmina           1     .       .     1    1      1       .
## Moshe             1     .       1     .    .      .       1
## Nang              .     .       1     .    .      1       1
## Samira            .     .       1     .    1      .       .
## Ibrahim           .     .       .     1    1      .       .</code></pre>
<p>Por ejemplo, Carmina (<code>1, 0, 0, 1, 1, 1, 0</code>) está
directamente conectada con Alejandra (que tiene el índice 1), Moshe
(índice 4), Nang (índice 5), Samira (índice 6) y , pero no con Bruno
(índice 2) ni con Ibrahim (índice 7).</p>
</div>
<div id="diseños-y-graficación" class="section level2">
<h2>Diseños y graficación</h2>
<p>Un grafo es un objeto matemático abstracto sin una representación
específica en el espacio 2D, 3D o cualquier espacio geométrico. Esto
significa que, cuando queremos visualizar un grafo, primero tenemos que
encontrar una correspondencia entre los vértices y las coordenadas en un
espacio bidimensional o tridimensional, preferiblemente de una manera
útil y/o agradable a la vista. Una rama separada de la teoría de grafos,
denominada dibujo de grafos, trata de resolver este problema mediante
varios algoritmos de diseño de grafos. igraph implementa varios
algoritmos de diseño y también es capaz de dibujarlos en la pantalla o
en cualquier formato de salida que soporte el propio R.</p>
<div id="algoritmos-de-diseño" class="section level3">
<h3>Algoritmos de diseño</h3>
<p>Las funciones de diseño en igraph siempre empiezan por
<code>layout</code>. La siguiente tabla las resume:</p>
<table>
<colgroup>
<col width="23%" />
<col width="76%" />
</colgroup>
<thead>
<tr class="header">
<th>Nombre del método</th>
<th>Descripción del algoritmo</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>layout_randomly</code></td>
<td>Coloca los vértices de forma totalmente aleatoria</td>
</tr>
<tr class="even">
<td><code>layout_in_circle</code></td>
<td>Disposición determinista que coloca los vértices en un círculo</td>
</tr>
<tr class="odd">
<td><code>layout_on_sphere</code></td>
<td>Disposición determinista que coloca los vértices de manera uniforme
en la superficie de una esfera</td>
</tr>
<tr class="even">
<td><code>layout_with_drl</code></td>
<td>El algoritmo DRL (<em>Distributed Recursive Layout</em>) para grafos
grandes</td>
</tr>
<tr class="odd">
<td><code>layout_with_fr</code></td>
<td>El algoritmo dirigido Fruchterman-Reingold</td>
</tr>
<tr class="even">
<td><code>layout_with_kk</code></td>
<td>El algoritmo dirigido Kamada-Kawai</td>
</tr>
<tr class="odd">
<td><code>layout_with_lgl</code></td>
<td>El algoritmo LGL (<em>Large Graph Layout</em>) para grafos
grandes</td>
</tr>
<tr class="even">
<td><code>layout_as_tree</code></td>
<td>Diseño de árbol de Reingold-Tilford, útil para grafos (casi)
arbóreos</td>
</tr>
<tr class="odd">
<td><code>layout_nicely</code></td>
<td>Algoritmo de diseño que elige automáticamente uno de los otros
algoritmos en función de determinadas propiedades del grafo</td>
</tr>
</tbody>
</table>
<p>Los algoritmos de diseño pueden ejecutarse directamente con un grafo
como primer argumento. Devolverán una matriz con dos columnas y tantas
filas como número de vértices del grafo; cada fila corresponderá a la
posición de un único vértice, ordenado según el ID del vértice. Algunos
algoritmos tienen una variante 3D; en este caso devuelven tres columnas
en lugar de 2.</p>
<div class="sourceCode" id="cb101"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb101-1"><a href="#cb101-1" tabindex="-1"></a>layout <span class="ot">&lt;-</span> <span class="fu">layout_with_kk</span>(g)</span></code></pre></div>
<p>Algunos algoritmos de diseño toman argumentos adicionales; por
ejemplo, cuando se diseña un grafo con la forma de un árbol, puede tener
sentido especificar qué vértice debe colocarse en la raíz del
diseño:</p>
<div class="sourceCode" id="cb102"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb102-1"><a href="#cb102-1" tabindex="-1"></a>layout <span class="ot">&lt;-</span> <span class="fu">layout_as_tree</span>(g, <span class="at">root =</span> <span class="dv">2</span>)</span></code></pre></div>
</div>
<div id="dibujar-un-grafo-utilizando-un-diseño" class="section level3">
<h3>Dibujar un grafo utilizando un diseño</h3>
<p>Podemos trazar nuestra red social imaginaria con el algoritmo de
diseño Kamada-Kawai de la siguiente manera:</p>
<div class="sourceCode" id="cb103"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb103-1"><a href="#cb103-1" tabindex="-1"></a>layout <span class="ot">&lt;-</span> <span class="fu">layout_with_kk</span>(g)</span></code></pre></div>
<div class="sourceCode" id="cb104"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb104-1"><a href="#cb104-1" tabindex="-1"></a><span class="fu">plot</span>(g, <span class="at">layout =</span> layout, <span class="at">main =</span> <span class="st">&quot;Red social con el algoritmo de diseño Kamada-Kawai&quot;</span>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>Esto debería abrir una nueva ventana mostrando una representación
visual de la red. Recuerda que la ubicación exacta de los nodos puede
ser diferente en tu máquina, ya que la disposición no es
determinista.</p>
<p>El argumento <code>layout</code> también acepta funciones; en este
caso, la función será llamada con el grafo como su primer argumento.
Esto permite ingresar directamente el nombre de una función de diseño,
sin tener que crear una variable de diseño, como en el ejemplo
anterior:</p>
<div class="sourceCode" id="cb105"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb105-1"><a href="#cb105-1" tabindex="-1"></a><span class="fu">plot</span>(</span>
<span id="cb105-2"><a href="#cb105-2" tabindex="-1"></a>  g, </span>
<span id="cb105-3"><a href="#cb105-3" tabindex="-1"></a>  <span class="at">layout =</span> layout_with_fr,</span>
<span id="cb105-4"><a href="#cb105-4" tabindex="-1"></a>  <span class="at">main =</span> <span class="st">&quot;Red social con el algoritmo de disposición Fruchterman-Reingold&quot;</span></span>
<span id="cb105-5"><a href="#cb105-5" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>Para mejorar el aspecto visual, una adición trivial sería colorear
los vértices según el género. También deberíamos intentar colocar los
nombres ligeramente fuera de los vértices para mejorar la
legibilidad:</p>
<div class="sourceCode" id="cb106"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb106-1"><a href="#cb106-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>color <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(<span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="sc">==</span> <span class="st">&quot;m&quot;</span>, <span class="st">&quot;yellow&quot;</span>, <span class="st">&quot;red&quot;</span>)</span>
<span id="cb106-2"><a href="#cb106-2" tabindex="-1"></a><span class="fu">plot</span>(</span>
<span id="cb106-3"><a href="#cb106-3" tabindex="-1"></a>  g, </span>
<span id="cb106-4"><a href="#cb106-4" tabindex="-1"></a>  <span class="at">layout =</span> layout, </span>
<span id="cb106-5"><a href="#cb106-5" tabindex="-1"></a>  <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>,</span>
<span id="cb106-6"><a href="#cb106-6" tabindex="-1"></a>  <span class="at">main =</span> <span class="st">&quot;Red social - con los géneros como colores&quot;</span></span>
<span id="cb106-7"><a href="#cb106-7" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>También puedes tratar el atributo <code>gender</code> como un factor
y proporcionar los colores como un argumento a <code>plot()</code>, que
tiene prioridad sobre el atributo <code>color</code> que se asigna de
manera estándar a los vértices. Los colores se asignan
automáticamente:</p>
<div class="sourceCode" id="cb107"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb107-1"><a href="#cb107-1" tabindex="-1"></a><span class="fu">plot</span>(</span>
<span id="cb107-2"><a href="#cb107-2" tabindex="-1"></a>  g, </span>
<span id="cb107-3"><a href="#cb107-3" tabindex="-1"></a>  <span class="at">layout =</span> layout, </span>
<span id="cb107-4"><a href="#cb107-4" tabindex="-1"></a>  <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>, </span>
<span id="cb107-5"><a href="#cb107-5" tabindex="-1"></a>  <span class="at">vertex.color =</span> <span class="fu">as.factor</span>(<span class="fu">V</span>(g)<span class="sc">$</span>gender))</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>Como se vio anteriormente, con el argumento <code>vertex.color</code>
puedes especificar propiedades visuales para <code>plot</code> en lugar
de usar y/o manipular los atributos de vértices o aristas. El siguiente
gráfico muestra las relaciones formales con líneas gruesas y las
informales con líneas finas:</p>
<div class="sourceCode" id="cb108"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb108-1"><a href="#cb108-1" tabindex="-1"></a><span class="fu">plot</span>(</span>
<span id="cb108-2"><a href="#cb108-2" tabindex="-1"></a>  g,</span>
<span id="cb108-3"><a href="#cb108-3" tabindex="-1"></a>  <span class="at">layout =</span> layout,</span>
<span id="cb108-4"><a href="#cb108-4" tabindex="-1"></a>  <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>,</span>
<span id="cb108-5"><a href="#cb108-5" tabindex="-1"></a>  <span class="at">vertex.size =</span> <span class="dv">20</span>,</span>
<span id="cb108-6"><a href="#cb108-6" tabindex="-1"></a>  <span class="at">vertex.color =</span> <span class="fu">ifelse</span>(<span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="sc">==</span> <span class="st">&quot;m&quot;</span>, <span class="st">&quot;yellow&quot;</span>, <span class="st">&quot;red&quot;</span>),</span>
<span id="cb108-7"><a href="#cb108-7" tabindex="-1"></a>  <span class="at">edge.width =</span> <span class="fu">ifelse</span>(<span class="fu">E</span>(g)<span class="sc">$</span>is_formal, <span class="dv">5</span>, <span class="dv">1</span>)</span>
<span id="cb108-8"><a href="#cb108-8" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>Este último procedimiento es preferible si quieres modificar la
representación visual de tu grafo, pero no quieres hacer modificaciones
al grafo mismo.</p>
<p>En resumen, hay propiedades especiales de vértices y aristas que
corresponden a la representación visual del grafo. Estos atributos
pueden modificar la configuración predeterminada de igraph (es decir,
color, peso, nombre, forma, diseño, etc.). Las dos tablas siguientes
resumen los atributos visuales más utilizados para vértices y aristas,
respectivamente:</p>
</div>
<div id="atributos-de-los-vértices-para-graficar" class="section level3">
<h3>Atributos de los vértices para graficar</h3>
<table>
<colgroup>
<col width="23%" />
<col width="23%" />
<col width="53%" />
</colgroup>
<thead>
<tr class="header">
<th>Nombre del atributo</th>
<th>Argumento</th>
<th>Propósito</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>color</code></td>
<td><code>vertex.color</code></td>
<td>Color del vértice</td>
</tr>
<tr class="even">
<td><code>label</code></td>
<td><code>vertex.label</code></td>
<td>Etiqueta del vértice. Se convertirán en caracteres. Especifique NA
para omitir las etiquetas de los vértices. Las etiquetas de vértices por
defecto son los IDs de los vértices.</td>
</tr>
<tr class="odd">
<td><code>label.cex</code></td>
<td><code>vertex.label.cex</code></td>
<td>Tamaño de fuente de la etiqueta del vértice, interpretado como un
factor multiplicativo, de forma similar a la función <code>text</code>
de R</td>
</tr>
<tr class="even">
<td><code>label.color</code></td>
<td><code>vertex.label.color</code></td>
<td>Color de la etiqueta del vértice</td>
</tr>
<tr class="odd">
<td><code>label.degree</code></td>
<td><code>vertex.label.degree</code></td>
<td>Define la posición de las etiquetas de los vértices, en relación con
el centro de los mismos. Se interpreta como un ángulo en radianes, cero
significa ‘a la derecha’, y ‘pi’ significa a la izquierda, arriba es
-pi/2 y abajo es pi/2. El valor por defecto es -pi/4</td>
</tr>
<tr class="even">
<td><code>label.dist</code></td>
<td><code>vertex.label.dist</code></td>
<td>Distancia de la etiqueta del vértice desde el propio vértice, en
relación con el tamaño del vértice</td>
</tr>
<tr class="odd">
<td><code>label.family</code></td>
<td><code>vertex.label.family</code></td>
<td>Familia tipográfica del vértice, de forma similar a la función
<code>text</code> de R</td>
</tr>
<tr class="even">
<td><code>label.font</code></td>
<td><code>vertex.label.font</code></td>
<td>Fuente dentro de la familia de fuentes del vértice, de forma similar
a la función <code>text</code> de R</td>
</tr>
<tr class="odd">
<td><code>shape</code></td>
<td><code>vertex.shape</code></td>
<td>La forma del vértice, actualmente “circle”, “square”, “csquare”,
“rectangle”, “crectangle”, “vrectangle”, “pie” (consultar
<code>vertex.shape.pie</code>), ‘sphere’ y “none” son admitidos, y sólo
por el comando <code>plot.igraph</code></td>
</tr>
<tr class="even">
<td><code>size</code></td>
<td><code>vertex.size</code></td>
<td>El tamaño del vértice, un escalar numérico o vector, en este último
caso el tamaño de cada vértice puede ser diferente</td>
</tr>
</tbody>
</table>
</div>
<div id="atributos-de-las-aristas-para-graficar" class="section level3">
<h3>Atributos de las aristas para graficar</h3>
<table>
<colgroup>
<col width="34%" />
<col width="40%" />
<col width="25%" />
</colgroup>
<thead>
<tr class="header">
<th>Nombre del atributo</th>
<th>Argumento</th>
<th>Propósito</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>color</code></td>
<td><code>edge.color</code></td>
<td>Color de la arista</td>
</tr>
<tr class="even">
<td><code>curved</code></td>
<td><code>edge.curved</code></td>
<td>Un valor numérico especifica la curvatura de la arista; una
curvatura cero significa aristas rectas, valores negativos significan
que la arista se curva en el sentido de las agujas del reloj, valores
positivos lo contrario. TRUE significa curvatura 0.5, FALSE significa
curvatura cero</td>
</tr>
<tr class="odd">
<td><code>arrow.size</code></td>
<td><code>edge.arrow.size</code></td>
<td>Actualmente es una constante, por lo que es la misma para todas las
aristas. Si se presenta un vector, sólo se utiliza el primer elemento,
es decir, si se toma de un atributo de aristas, sólo se utiliza el
atributo de la primera arista para todas las flechas</td>
</tr>
<tr class="even">
<td><code>arrow.width</code></td>
<td><code>edge.arrow.width</code></td>
<td>El ancho de las flechas. Actualmente es una constante, por lo que es
la misma para todas las aristas</td>
</tr>
<tr class="odd">
<td><code>width</code></td>
<td><code>edge.width</code></td>
<td>Anchura del borde en píxeles</td>
</tr>
<tr class="even">
<td><code>label</code></td>
<td><code>edge.label</code></td>
<td>Si se especifica, añade una etiqueta al borde</td>
</tr>
<tr class="odd">
<td><code>label.cex</code></td>
<td><code>edge.label.cex</code></td>
<td>Tamaño de fuente de la etiqueta de la arista, interpretado como un
factor multiplicativo, de forma similar a la función <code>text</code>
de R</td>
</tr>
<tr class="even">
<td><code>label.color</code></td>
<td><code>edge.label.color</code></td>
<td>Color de la etiqueta de la arista</td>
</tr>
<tr class="odd">
<td><code>label.family</code></td>
<td><code>edge.label.family</code></td>
<td>Familia tipográfica de la arista, de forma similar a la función
<code>text</code> de R</td>
</tr>
<tr class="even">
<td><code>label.font</code></td>
<td><code>edge.label.font</code></td>
<td>Fuente dentro de la familia de fuentes de la arista, de forma
similar a la función <code>text</code> de R</td>
</tr>
</tbody>
</table>
</div>
<div id="argumentos-más-comunes-de-plot" class="section level3">
<h3>Argumentos más comunes de <code>plot()</code></h3>
<p>Estos parámetros pueden especificarse como argumentos de la función
<code>plot</code> para ajustar el aspecto general del gráfico.</p>
<table>
<colgroup>
<col width="44%" />
<col width="55%" />
</colgroup>
<thead>
<tr class="header">
<th>Argumento</th>
<th>Propósito</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>layout</code></td>
<td>El diseño que se va a utilizar. Puede ser una instancia de
<code>layout</code>, una lista de tuplas que contengan coordenadas X-Y,
o el nombre de un algoritmo de diseño. El valor por defecto es
<code>auto</code>, que selecciona un algoritmo de diseño automáticamente
basado en el tamaño y la conectividad del grafo.</td>
</tr>
<tr class="even">
<td><code>margin</code></td>
<td>La cantidad de espacio vacío debajo, encima, a la izquierda y a la
derecha del gráfico, es un vector numérico de longitud cuatro</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="igraph-y-el-mundo-exterior" class="section level2">
<h2>igraph y el mundo exterior</h2>
<p>Ningún módulo de grafos estaría completo sin algún tipo de
funcionalidad de importación/exportación que permita al paquete
comunicarse con programas y kits de herramientas externos. igraph no es
una excepción: proporciona funciones para leer los formatos de grafos
más comunes y para guardar grafos en archivos que obedezcan estas
especificaciones de formato. Las funciones principales para leer y
escribir de/a un fichero son <code>read_graph()</code> y
<code>write_graph()</code>, respectivamente. La siguiente tabla resume
los formatos que igraph puede leer o escribir:</p>
<table>
<colgroup>
<col width="23%" />
<col width="23%" />
<col width="26%" />
<col width="26%" />
</colgroup>
<thead>
<tr class="header">
<th>Formato</th>
<th>Nombre corto</th>
<th>Método de lectura</th>
<th>Método de escritura</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Lista de adyacencia (a.k.a. <a href="https://lgl.sourceforge.net/#FileFormat">LGL</a>)</td>
<td><code>lgl</code></td>
<td><code>read_graph(file, format = c(&quot;lgl&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;lgl&quot;))</code></td>
</tr>
<tr class="even">
<td>Matriz de adyacencia</td>
<td><code>adjacency</code></td>
<td><code>graph_from_adjacency_matrix(adjmatrix, mode = c(&quot;directed&quot;, &quot;undirected&quot;, &quot;max&quot;, &quot;min&quot;, &quot;upper&quot;,&quot;lower&quot;, &quot;plus&quot;), weighted = NULL, diag = TRUE, add.colnames = NULL, add.rownames = NA)</code></td>
<td><code>as.matrix(graph, &quot;adjacency&quot;)</code></td>
</tr>
<tr class="odd">
<td>DIMACS</td>
<td><code>dimacs</code></td>
<td><code>read_graph(file, format = c(&quot;dimacs&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;dimacs&quot;))</code></td>
</tr>
<tr class="even">
<td>Edge list</td>
<td><code>edgelist</code></td>
<td><code>read_graph(file, format = c(&quot;edgelist&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;edgelist&quot;))</code></td>
</tr>
<tr class="odd">
<td><a href="https://www.graphviz.org">GraphViz</a></td>
<td><code>dot</code></td>
<td>not supported yet</td>
<td><code>write_graph(graph, file, format = c(&quot;dot&quot;))</code></td>
</tr>
<tr class="even">
<td>GML</td>
<td><code>gml</code></td>
<td><code>read_graph(file, format = c(&quot;gml&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;gml&quot;))</code></td>
</tr>
<tr class="odd">
<td>GraphML</td>
<td><code>graphml</code></td>
<td><code>read_graph(file, format = c(&quot;graphml&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;graphml&quot;))</code></td>
</tr>
<tr class="even">
<td>LEDA</td>
<td><code>leda</code></td>
<td>not supported yet</td>
<td><code>write_graph(graph, file, format = c(&quot;leda&quot;))</code></td>
</tr>
<tr class="odd">
<td>Labeled edgelist (a.k.a. <a href="https://lgl.sourceforge.net/#FileFormat">NCOL</a>)</td>
<td><code>ncol</code></td>
<td><code>read_graph(file, format = c(&quot;ncol&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;ncol&quot;))</code></td>
</tr>
<tr class="even">
<td><a href="http://mrvar.fdv.uni-lj.si/pajek/">Pajek</a> format</td>
<td><code>pajek</code></td>
<td><code>read_graph(file, format = c(&quot;pajek&quot;))</code></td>
<td><code>write_graph(graph, file, format = c(&quot;pajek&quot;))</code></td>
</tr>
</tbody>
</table>
<hr />
<p><strong>NOTA:</strong> La mayoría de los formatos tienen sus propias
limitaciones; por ejemplo, no todos pueden almacenar atributos. Tu mejor
opción es probablemente GraphML o GML si quieres guardar los grafos de
igraph en un formato que pueda ser leído desde un paquete externo y
quieres preservar los atributos numéricos y de cadena. <em>Edge
list</em> y NCOL también están bien si no tienes atributos (aunque NCOL
admite nombres de vértices y pesos de aristas).</p>
<hr />
</div>
<div id="dónde-ir-a-continuación" class="section level2">
<h2>Dónde ir a continuación</h2>
<p>Este tutorial es una breve introducción a <code>igraph</code> en R.
Esperamos que hayas disfrutado de su lectura y que te resulte útil para
tus propios análisis de redes.</p>
<p>Para una descripción detallada de funciones específicas, consulta <a href="https://r.igraph.org/reference/" class="uri">https://r.igraph.org/reference/</a>. Si tienes preguntas
sobre cómo utilizar <code>igraph</code>, visita nuestro <a href="https://igraph.discourse.group">Foro</a>. Para informar de un
error, abre una <a href="https://github.com/igraph/rigraph/issues">incidencia en
Github</a>. Por favor, no hagas preguntas de uso en Github directamente,
ya que está pensado para desarrolladores y no para usuarios.</p>
</div>
<div id="información-de-la-sesión" class="section level2">
<h2>Información de la sesión</h2>
<p>En favor de la reproducibilidad, la información de la sesión para el
código anterior es la siguiente:</p>
<div class="sourceCode" id="cb109"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb109-1"><a href="#cb109-1" tabindex="-1"></a><span class="fu">sessionInfo</span>()</span></code></pre></div>
<pre><code>## R version 4.5.2 (2025-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Tahoe 26.2
## 
## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Europe/Zurich
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] igraph_2.2.2
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.39   R6_2.6.1        fastmap_1.2.0   Matrix_1.7-4   
##  [5] xfun_0.56       lattice_0.22-9  magrittr_2.0.4  glue_1.8.0     
##  [9] cachem_1.1.0    knitr_1.51      pkgconfig_2.0.3 htmltools_0.5.9
## [13] rmarkdown_2.30  lifecycle_1.0.5 cli_3.6.5       grid_4.5.2     
## [17] vctrs_0.7.1     sass_0.4.10     jquerylib_0.1.4 compiler_4.5.2 
## [21] tools_4.5.2     pillar_1.11.1   evaluate_1.0.5  bslib_0.10.0   
## [25] yaml_2.3.12     otel_0.2.0      rlang_1.1.7     jsonlite_2.0.0</code></pre>
</div>



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