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


<div id="TOC">
<ul>
<li><a href="#installation" id="toc-installation">Installation</a></li>
<li><a href="#usage" id="toc-usage">Usage</a></li>
<li><a href="#creating-a-graph" id="toc-creating-a-graph">Creating a
graph</a></li>
<li><a href="#vertex-and-edge-ids" id="toc-vertex-and-edge-ids">Vertex
and edge IDs</a></li>
<li><a href="#addingdeleting-vertices-and-edges" id="toc-addingdeleting-vertices-and-edges">Adding/deleting vertices and
edges</a></li>
<li><a href="#constructing-graphs" id="toc-constructing-graphs">Constructing graphs</a></li>
<li><a href="#setting-and-retrieving-attributes" id="toc-setting-and-retrieving-attributes">Setting and retrieving
attributes</a></li>
<li><a href="#structural-properties-of-graphs" id="toc-structural-properties-of-graphs">Structural properties of
graphs</a></li>
<li><a href="#querying-vertices-and-edges-based-on-attributes" id="toc-querying-vertices-and-edges-based-on-attributes">Querying
vertices and edges based on attributes</a>
<ul>
<li><a href="#selecting-vertices" id="toc-selecting-vertices">Selecting
vertices</a></li>
<li><a href="#selecting-edges" id="toc-selecting-edges">Selecting
edges</a></li>
</ul></li>
<li><a href="#treating-a-graph-as-an-adjacency-matrix" id="toc-treating-a-graph-as-an-adjacency-matrix">Treating a graph as an
adjacency matrix</a></li>
<li><a href="#layouts-and-plotting" id="toc-layouts-and-plotting">Layouts and plotting</a>
<ul>
<li><a href="#layout-algorithms" id="toc-layout-algorithms">Layout
algorithms</a></li>
<li><a href="#drawing-a-graph-using-a-layout" id="toc-drawing-a-graph-using-a-layout">Drawing a graph using a
layout</a></li>
<li><a href="#vertex-attributes-controlling-graph-plots" id="toc-vertex-attributes-controlling-graph-plots">Vertex attributes
controlling graph plots</a></li>
<li><a href="#edge-attributes-controlling-graph-plots" id="toc-edge-attributes-controlling-graph-plots">Edge attributes
controlling graph plots</a></li>
<li><a href="#generic-arguments-of-plot" id="toc-generic-arguments-of-plot">Generic arguments of
<code>plot()</code></a></li>
</ul></li>
<li><a href="#igraph-and-the-outside-world" id="toc-igraph-and-the-outside-world">igraph and the outside
world</a></li>
<li><a href="#where-to-go-next" id="toc-where-to-go-next">Where to go
next</a></li>
<li><a href="#session-info" id="toc-session-info">Session info</a></li>
</ul>
</div>

<p><code>igraph</code> is a fast and open source library for the
analysis of graphs or networks. The library consists of a core written
in C and bindings for high-level languages including <a href="https://r.igraph.org/">R</a>, <a href="https://python.igraph.org/en/stable/">Python</a>, and <a href="http://szhorvat.net/pelican/igraphm-a-mathematica-interface-for-igraph.html">Mathematica</a>.
This vignette aims to give you an overview of the functions available in
the R interface of <code>igraph</code>. For detailed function by
function API documentation, check out <a href="https://r.igraph.org/reference/" class="uri">https://r.igraph.org/reference/</a>.</p>
<hr />
<p><strong>NOTE:</strong> Throughout this tutorial, we will use words
<code>graph</code> and <code>network</code> as synonyms, and also
<code>vertex</code> or <code>node</code> as synonyms.</p>
<hr />
<div id="installation" class="section level2">
<h2>Installation</h2>
<p>To install the library from CRAN, use:</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>More details on dependencies, requirements, and troubleshooting on
installation are found on the main <a href="https://r.igraph.org/">documentation page</a>.</p>
</div>
<div id="usage" class="section level2">
<h2>Usage</h2>
<p>To use <code>igraph</code> in your R code, you must first load the
library:</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>
<pre><code>## 
## Attaching package: &#39;igraph&#39;</code></pre>
<pre><code>## The following objects are masked from &#39;package:stats&#39;:
## 
##     decompose, spectrum</code></pre>
<pre><code>## The following object is masked from &#39;package:base&#39;:
## 
##     union</code></pre>
<p>Now you have all <code>igraph</code> functions available.</p>
</div>
<div id="creating-a-graph" class="section level2">
<h2>Creating a graph</h2>
<p><code>igraph</code> offers many ways to create a graph. The simplest
one is the function <code>make_empty_graph()</code>:</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 class="ot">&lt;-</span> <span class="fu">make_empty_graph</span>()</span></code></pre></div>
<p>The most common way to create a graph is <code>make_graph()</code>,
which constructs a network based on specified edges. For example, to
make a graph with 10 nodes (numbered <code>1</code> to <code>10</code>)
and two edges connecting nodes <code>1-2</code> and
<code>1-5</code>:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" 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>Starting from igraph 0.8.0, you can also include literal here, via
igraph’s formula notation. In this case, the first term of the formula
has to start with a <code>~</code> character, just like regular formulae
in R. The expressions consist of vertex names and edge operators. An
edge operator is a sequence of <code>-</code> and <code>+</code>
characters, the former is for the edges and the latter is used for arrow
heads. The edges can be arbitrarily long, that is to say, you may use as
many <code>-</code> characters to “draw” them as you like. If all edge
operators consist of only <code>-</code> characters then the graph will
be undirected, whereas a single <code>+</code> character implies a
directed graph: that is to say to create the same graph as above:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" 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>We can print the graph to get a summary of its nodes and edges:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>g</span></code></pre></div>
<pre><code>## IGRAPH 374dcc0 UN-- 10 2 -- 
## + attr: name (v/c)
## + edges from 374dcc0 (vertex names):
## [1] 1--2 1--5</code></pre>
<p>This means: <strong>U</strong>ndirected <strong>N</strong>amed graph
with <strong>10</strong> vertices and <strong>2</strong> edges, with the
exact edges listed out. If the graph has a <code>[name]</code>
attribute, it is printed as well.</p>
<hr />
<p><strong>NOTE</strong>: <code>summary()</code> does not list the
edges, which is convenient for large graphs with millions of edges:</p>
<hr />
<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">summary</span>(g)</span></code></pre></div>
<pre><code>## IGRAPH 374dcc0 UN-- 10 2 -- 
## + attr: name (v/c)</code></pre>
<p>The same function <code>make_graph()</code> can create some notable
graphs by just specifying their name. For example you can create the
graph that represents the social network of Zachary’s karate club, that
shows the friendship between 34 members of a karate club at a US
university in the 1970s:</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">make_graph</span>(<span class="st">&quot;Zachary&quot;</span>)</span></code></pre></div>
<p>To visualize a graph you can use <code>plot()</code>:</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><span class="fu">plot</span>(g)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>A more detailed description of plotting options is provided later on
in this tutorial.</p>
</div>
<div id="vertex-and-edge-ids" class="section level2">
<h2>Vertex and edge IDs</h2>
<p>Vertices and edges have numerical vertex IDs in igraph. Vertex IDs
are always consecutive and they start with 1. For a graph with n
vertices the vertex IDs are always between 1 and n. If some operation
changes the number of vertices in the graphs, for instance a subgraph is
created via <code>induced_subgraph()</code>, then the vertices are
renumbered to satisfy this criterion.</p>
<p>The same is true for the edges as well: edge IDs are always between 1
and m, the total number of edges in the graph.</p>
<hr />
<p><strong>NOTE</strong>: If you are familiar with the C core or the <a href="https://python.igraph.org/en/stable/">Python</a> interface of
<code>igraph</code>, you might have noticed that in those languages
vertex and edge IDs start from 0. In the R interface, both start from 1
instead, to keep consistent with the convention in each language.</p>
<hr />
<p>In addition to IDs, vertices and edges can be assigned a name and
other attributes. That makes it easier to track them whenever the graph
is altered. Examples of this pattern are shown later on in this
tutorial.</p>
</div>
<div id="addingdeleting-vertices-and-edges" class="section level2">
<h2>Adding/deleting vertices and edges</h2>
<p>Let’s continue working with the Karate club graph. To add one or more
vertices to an existing graph, use <code>add_vertices()</code>:</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_vertices</span>(g, <span class="dv">3</span>)</span></code></pre></div>
<p>Similarly, to add edges you can use <code>add_edges()</code>:</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" 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>Edges are added by specifying the source and target vertex IDs for
each edge. This call added three edges, one connecting vertices
<code>1</code> and <code>35</code>, one connecting vertices
<code>1</code> and <code>36</code>, and one connecting vertices
<code>34</code> and <code>37</code>.</p>
<p>In addition to the <code>add_vertices()</code> and
<code>add_edges()</code> functions, the plus operator can be used to add
vertices or edges to graph. The actual operation that is performed
depends on the type of the right hand side argument:</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">+</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>You can add a single vertex/edge at a time using
<code>add_vertex()</code> and <code>add_edge()</code> (singular).</p>
<p><strong>Warning</strong>: If you need to add multiple edges to a
graph, it is much more efficient to call <code>add_edges()</code> once
rather than repeatedly calling <code>add_edge()</code> with a single new
edge. The same applies when deleting edges and vertices.</p>
<p>If you try to add edges to vertices with invalid IDs (i.e., you try
to add an edge to vertex <code>38</code> when the graph has only 37
vertices), <code>igraph</code> shows an error:</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" 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>Let us add some more vertices and edges to our graph. In
<code>igraph</code> we can use the <code>magrittr</code> package, which
provides a mechanism for chaining commands with the operator
<code>%&gt;%</code>:</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> g <span class="sc">%&gt;%</span></span>
<span id="cb20-2"><a href="#cb20-2" 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="cb20-3"><a href="#cb20-3" tabindex="-1"></a>  <span class="fu">add_vertices</span>(<span class="dv">3</span>) <span class="sc">%&gt;%</span></span>
<span id="cb20-4"><a href="#cb20-4" 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="cb20-5"><a href="#cb20-5" tabindex="-1"></a>g</span></code></pre></div>
<pre><code>## IGRAPH c97b34d U--- 40 86 -- Zachary
## + attr: name (g/c)
## + edges from c97b34d:
##  [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>We now have an undirected graph with 40 vertices and 86 edges. Vertex
and edge IDs are always <em>contiguous</em>, so if you delete a vertex
all subsequent vertices will be renumbered. When a vertex is renumbered,
edges are <strong>not</strong> renumbered, but their source and target
vertices will be. Use <code>delete_vertices()</code> and
<code>delete_edges()</code> to perform these operations. For instance,
to delete the edge connecting vertices <code>1-34</code>, get its ID and
then delete it:</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>edge_id_to_delete <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="cb22-2"><a href="#cb22-2" tabindex="-1"></a>edge_id_to_delete</span></code></pre></div>
<pre><code>## [1] 82</code></pre>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">delete_edges</span>(g, edge_id_to_delete)</span></code></pre></div>
<p>As an example, to create a broken ring:</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>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="cb25-2"><a href="#cb25-2" 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>The example above shows that you can also refer to edges with strings
containing the IDs of the source and target vertices, connected by a
pipe symbol <code>|</code>. <code>&quot;10|1&quot;</code> in the above example
means the edge that connects vertex 10 to vertex 1. Of course you can
also use the edge IDs directly, or retrieve them with the
<code>get_edge_ids()</code> function:</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_ring</span>(<span class="dv">5</span>)</span>
<span id="cb26-2"><a href="#cb26-2" 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="cb26-3"><a href="#cb26-3" tabindex="-1"></a><span class="fu">plot</span>(g)</span></code></pre></div>
<p><img role="img" 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xd7bNvR40wnH/DAy8aM3/qF0m97dca4LV982f2bujFmRFMMGzwgnnzyyU097D4CBDYSKP9G5nPLXoxxW/X8v9Ob7t0trp87Kd7/36+NY794Vkz/8/iNztT9t5O2WRflv/++aifQc8VqN5czESDQR4Gnn346th+9NvFZ5pXaYRO3WRaHTHyqqmN32npIlOfyRYBA9wJPPfVUjNt2RPeD/u/Rn33wqrjtU9+L80+bUfoYjMFx9hUnxYOLt6jq2B1Gt8TTTz5e1ViDqhMQgKpzMopAXQisWrUyhg9K/o7P3//t/vHZN84o/XpuddsYPrgpVq+u7jfHqjujUQTyKbBq1ar1z5hWu7vtNl8Zbz10Xvzywz+JzYevje/eemBVhw4vvT/pypXLqxprUHUCVf5zWN3JjCJAoH8FRo/eLFa2JPtrW37x84HjF8ZBExZWvbiVpY/IGDlyZNXjDSRQVIHy35NVa3tuS1f67Lbtsjhhn4fjL4u2qnxok7dXryu9Fmj05pt8zJ29E0j2L2nv5nAUAQI1Ehg3blw88cKwqs/2xHOjY85j26//FfiqDyoNfGzxmthll12SHGIsgUIKlP+eLFi8MsofMJz0a79dFsZmw0rJpoqvBc8PjfETdq1ipCHVCghA1UoZR6AOBA444IC474n2qlaycu2g+H//u0+cc+L/vjR+xdrBsWzl0Jdub+qbJ5/riKFDh8XYsWM39bD7CBDYSGDYsGExcdz2MX9h8gB0z+PbxQGlZ2er+Zr3dHPsv//+1Qw1pkoBAahKKMMI1IPApEmTor1pcDz8TPdPubd3NMW5Pzku9tphacyaPz6m/2lC/OquSfHpnx8Zo4Z1/54jM+d3xtFHH10P27UGAg0hcPRxr4mZDzR1u9ZHl4yJq+/YK1ra/vq/3fJ7Av259MsJG78nUFcneObFznj6udY48MDqXi/U1Xnc/3IB7wT9cg+3CNS9wNvPfFf89M5vxidO6joEld/48Kb7dlv/38YbOuuwuTGouevjymN/dvew+Or33rfxYb4nQKAbgTPf8Y/xxtf9OM4+srXLzwFb0zIwPlX6AeSSmybH6Yf8ufRJ8R3x9bNuiBFDWrs5818f+ukdTXH6m94Uzc3dv9Fijycy4GUC3gn6ZRxuEKh/gYULF8bee+4av/5oR+kzvrr/qTPpbm6+rz2+e9eEuOve+es/JT7p8cYTKKrAtCkHxYk73RtvPKTrkLJq3aB4buWwKP8mWE8/iGxwXL6m9IzslwbE7XPui1139RqgDS61+FMLrBaKzkEgRYHtt98+zv7Ah+JT1wyu6azlf2g/c/2guOjL3xR+airrZEUQKP+9ueiWQfHsiq5fC1R+tmfnLZdXHX7Kbp+5blCc8dYzhZ9+uIgEoH5AdUoC/S3wmfM/Gy2j9orzr63NM0DrWjtj30+si/d+4KNx1FFH9ffynZ9A7gQOOuig+MSnLogzvzcwVpXeRqIWX++8rC2ebNklLrrkK7U4nXNUCGiBVYC4SaBRBJ5//vnYfdfxcfxea+P8N/Q+CK0u/WP91u82xbGve2dcWPop1hcBAr0X+Md3vi1m3HhVXPOhpthseO//Xn7tNx1x6U0tsWLFCu/J1ftydHukZ4C65fEggfoVGDNmTDy9aEn86YVd4t9+NijKQSbp12NLO+K0bw2KVx1zRnzp0m8kPdx4AgQqBC6/4kfxlnd+MF77leb4y8Luf+Gg4tD1N8vPxl7++6Ex44lxsXjxYuFnU0g1uk8AqhGk0xDIQmDw4MExY9ZtsXz4fnH0hQPi/93WXlUQWrK8My64rine+M2BcdSJb4tvfOsyr/vJooDmzKXA575wUXzxy9+Nt18+OM771YBY+HzPP5ysa+uMn/2xLY67eGA8HEfEH+64x3tx9fPVoQXWz8BOT6A/BTo7O+Paa6+NiRMnxrp16+KC8z4RM2bOiml7DYmDdlodE7ZpitHDmqK19N6Jy1Z1lt4/KOIPC0bEn55sibPOfEec+/FPxv333x/lN3ObPHlyfy7VuQkUTuDZZ5+Ni770+bjssu/EHjsMjleNWxW7j+2MLUc2xZBBUfpA1M54bGln/PGJYTF7fmu86pCD4z/P+1xMmTKlcFZZbFgAykLdnARqJHDffffF448/HieddNJLz+AsXbo0br755pj92+nx4F/+HMuXLy+958iA2HqbbWLPvfeNw484Oo499tj1oae8jLVr18bVV18dxxxzTGy77bY1WpnTECCwQaClpSWmT58es2ZOj/l/mhtLnnkmWtvaYvToUbHbpL3j0KlHxgknnODv3wawlP4UgFKCNg2BWgu8+OKLcc0118Qpp5xS+od0dJ9Ov2DBgrj99tvjtNNOi4EDvT9qnzAdTIBAQwh4DVBDlMkiCbxcoNz6mjVrVpQ/G6yv4ad85vIHOm5TeobozjvvfPlEbhEgQCCnAgJQTgtrW/kWmDdvXukt9wfE3nvvXbONHnroofHwww+v/82Tmp3UiQgQIFCnAgJQnRbGsgh0JVBufc2dOzemTZv20ut+uhqb5P6hQ4fGYYcdtv6ZpbbS6xN8ESBAIM8CAlCeq2tvuRMot75mzpy5/lOha9H6qgQqt8K23nprrbBKGLcJEMidgACUu5LaUJ4Fyq2v8idC77XXXv22zfKv4GqF9RuvExMgUCcCAlCdFMIyCPQk8MILL8Q999xT89ZX5bxaYZUibhMgkEcBASiPVbWn3AnU+re+egLSCutJyOMECDS6gADU6BW0/kIIpNH6qoTUCqsUcZsAgTwJCEB5qqa95FIgrdZXJZ5WWKWI2wQI5ElAAMpTNe0ldwJpt74qAbXCKkXcJkAgLwICUF4qaR+5FMii9VUJqRVWKeI2AQJ5EBCA8lBFe8ilQFatr0pMrbBKEbcJEMiDgACUhyraQ+4Esm59VYJqhVWKuE2AQKMLCECNXkHrz6VAPbS+KmG1wipF3CZAoJEFBKBGrp6151KgXlpflbhaYZUibhMg0MgCAlAjV8/acydQb62vSmCtsEoRtwkQaFQBAahRK2fduRSox9ZXJbRWWKWI2wQINKKAANSIVbPmXArUa+urElsrrFLEbQIEGlFAAGrEqllz7gTqvfVVCa4VViniNgECjSYgADVaxaw3lwKN0PqqhC+3wh555JFYvHhx5UNuEyBAoO4FBKC6L5EF5l2g3PqaO3duTJs2LZqamhpmu+VW2NSpU2PWrFnR1tbWMOu2UAIECJQFBCDXAYEMBTa0vg488MAYPXp0hivp3dRaYb1zcxQBAtkLCEDZ18AKCizQiK2vynJphVWKuE2AQCMICECNUCVrzKVAo7a+KouhFVYp4jYBAo0gIAA1QpWsMXcCjd76qiyIVliliNsECNS7gABU7xWyvlwK5KH1VVmYDW+Q+Mwzz1Q+5DYBAgTqTkAAqruSWFDeBfLS+qqs04Y3SJw5c6bfCqvEcZsAgboTEIDqriQWlGeBvLW+Kmu1oRU2Z86cyofcJkCAQF0JCEB1VQ6LybtAHltflTUrt8Ieeuih0AqrlHGbAIF6EhCA6qka1pJrgby2viqLphVWKeI2AQL1KCAA1WNVrCl3AnlvfVUWTCusUsRtAgTqTUAAqreKWE8uBYrQ+qosnFZYpYjbBAjUk4AAVE/VsJZcChSl9VVZPK2wShG3CRCoJwEBqJ6qYS25Eyha66uygFphlSJuEyBQLwICUL1UwjpyKVDE1ldlIbXCKkXcJkCgHgQEoHqogjXkUqCora/KYm74rDBvkFgp4zYBAlkKCEBZ6ps7twJFb31VFnb8+PGx9dZbhzdIrJRxmwCBrAQEoKzkzZtrAa2vvy+vVtjfm7iHAIHsBASg7OzNnFMBra9NF1YrbNMu7iVAIBsBASgbd7PmVEDrq/vCaoV17+NRAgTSExCA0rM2UwEEtL56LrJWWM9GRhAg0P8CAlD/G5uhIAJaX9UVWiusOiejCBDoXwEBqH99nb0gAlpfyQqtFZbMy2gCBGovIADV3tQZCyig9ZW86Fphyc0cQYBA7QQEoNpZOlNBBbS+eld4rbDeuTmKAIHaCAhAtXF0loIKaH31rfBaYX3zczQBAr0XEIB6b+dIAqH11feLQCus74bOQIBAcgEBKLmZIwisF9D6qs2FoBVWG0dnIUAgmYAAlMzLaALrBbS+anshaIXV1tPZCBDoWUAA6tnICAJ/J6D19Xckfb5DK6zPhE5AgEACAQEoAZahBMoCWl/9cx1ohfWPq7MSILBpAQFo0y7uJbBJAa2vTbLU7M5yK2yrrbaKOXPm1OycTkSAAIFNCQhAm1JxH4EuBLS+uoCp4d1Tp06Nhx56KJ555pkantWpCBAg8HIBAejlHm4R6FJA66tLmpo+oBVWU04nI0CgCwEBqAsYdxPYWEDra2ON/v9eK6z/jc1AoOgCAlDRrwD7r0pA66sqppoO0gqrKaeTESBQISAAVYC4SaBSQOurUiSd21ph6TibhUBRBQSgolbevqsS0PqqiqnfBmmF9RutExMovIAAVPhLAEB3Alpf3emk85hWWDrOZiFQNAEBqGgVt9+qBbS+qqbq14EbWmGzZs2Ktra2fp3LyQkQKI6AAFScWttpAgGtrwRYKQwtt8K23HJLb5CYgrUpCBRFQAAqSqXtM5GA1lcirlQGa4WlwmwSAoUREIAKU2obrVZA66taqXTHaYWl6202AnkXEIDyXmH7SySg9ZWIK/XBWmGpk5uQQG4FBKDcltbGeiOg9dUbtXSP0QpL19tsBPIqIADltbL2lVhA6ysxWSYHaIVlwm5SArkTEIByV1Ib6o2A1ldv1LI7RissO3szE8iLgACUl0raR58EtL76xJfJwVphmbCblEBuBASg3JTSRnorsKH1dcQRR0RTU1NvT+O4lAW0wlIGNx2BnAkIQDkrqO0kE9i49TVq1KhkBxuduYBWWOYlsAACDSsgADVs6Sy8FgJaX7VQzPYcWmHZ+pudQKMKCECNWjnr7rOA1lefCeviBFphdVEGiyDQcAICUMOVzIJrIaD1VQvF+jmHVlj91MJKCDSKgADUKJWyzpoKaH3VlLMuTrahFbZkyZK6WI9FECBQ3wICUH3Xx+r6QUDrqx9Q6+CUG1phM2fOjLa2tjpYkSUQIFDPAgJQPVfH2mouoPVVc9K6OqFWWF2Vw2II1LWAAFTX5bG4WgtofdVatP7OpxVWfzWxIgL1KCAA1WNVrKlfBLS++oW17k6qFVZ3JbEgAnUpIADVZVksqtYCWl+1Fq3v82mF1Xd9rI5APQgIQPVQBWvodwGtr34nrrsJtMLqriQWRKCuBASguiqHxfSHgNZXf6jW/zm1wuq/RlZIIEsBAShLfXP3u4DWV78T1/UEWmF1XR6LI5CpgACUKb/J+1tA66u/hev//Fph9V8jKySQhYAAlIW6OVMR0PpKhbnuJym3wqZMmRLeILHuS2WBBFIVEIBS5TZZWgJaX2lJN8Y8EyZMiC233DLmzJnTGAu2SgIE+l1AAOp3YhNkIXDffffFwIEDY6+99spienPWoYBWWB0WxZIIZCggAGWIb+r+ESi3vu65556YNm1aNDU19c8kztpwAlphDVcyCybQrwICUL/yOnnaAlpfaYs31nxaYY1VL6sl0J8CAlB/6jp36gJaX6mTN9yEWmENVzILJtAvAgJQv7A6aRYCWl9ZqDfenBu3wtrb2xtvA1ZMgEBNBASgmjA6SdYCWl9ZV6Cx5t/QCrvzzjsba+FWS4BAzQQEoJpROlGWAlpfWeo35txaYY1ZN6smUCsBAahWks6TmYDWV2b0DT2xVlhDl8/iCfRZQADqM6ETZCmg9ZWlfuPPrRXW+DW0AwK9FRCAeivnuLoQ0PqqizI09CK0whq6fBZPoNcCAlCv6RyYtYDWV9YVyMf8WmH5qKNdEEgqIAAlFTO+LgS0vuqiDLlZhFZYbkppIwSqFhCAqqYysJ4EtL7qqRr5WEu5Ffbggw/GkiVL8rEhuyBAoFsBAahbHg/Wo4DWVz1WpfHXVG6FlUPQzJkzwxskNn497YBATwICUE9CHq8rgQ2tr4MOOihGjRpVV2uzmMYX2NAKmzNnTuNvxg4IEOhWQADqlseD9SawofW155571tvSrCcnAuVngR544AGtsJzU0zYIdCUgAHUl4/66E9D6qruS5HJBWmG5LKtNEfg7AQHo70jcUY8CWl/1WJX8rkkrLL+1tTMCGwQEoA0S/qxrAa2vui5PLhenFZbLstoUgZcEBKCXKHxTrwJaX/VamXyvSyss3/W1OwICkGugrgW0vuq6PLlfnFZY7ktsgwUWEIAKXPxG2LrWVyNUKd9r1ArLd33trrgCAlBxa1/3O9f6qvsSFWKBWmGFKLNNFlBAACpg0Rthy1pfjVCl4qxRK6w4tbbT4ggIQMWpdUPtVOurocpViMVqhRWizDZZIAEBqEDFbpStan01SqWKtU6tsGLV227zLyAA5b/GDbVDra+GKlfhFqsVVriS23COBQSgHBe3Ebem9dWIVSvWmrXCilVvu82vgACU39o23M60vhquZIVcsFZYIctu0zkUEIByWNRG3JLWVyNWrbhr1gorbu3tPD8CAlB+atnQO9H6aujyFXLxU6ZMiQcffDCWLFlSyP3bNIFGFxCAGr2COVi/1lcOiljALQwbNizKIWjmzJnR3t5eQAFbJtDYAgJQY9ev4Vev9dXwJSz0BrTCCl1+m29wAQGowQvY6MvX+mr0Clq/VphrgEBjCghAjVm3XKxa6ysXZSz8JrTCCn8JAGhQAQGoQQvX6MvW+mr0Clr/xgJaYRtr+J5AYwgIQI1Rp9ytUusrdyUt/Ia0wgp/CQBoMAEBqMEKloflan3loYr2UCmwoRU2a9YsvxVWieM2gToUEIDqsCh5XpLWV56ra2/lVtiYMWNizpw5MAgQqHMBAajOC5S35Wl95a2i9lMpUP6sMG+QWKniNoH6ExCA6q8muV2R1lduS2tjGwlohW2E4VsCdSwgANVxcfK0NK2vPFXTXnoS0ArrScjjBLIXEICyr0EhVqD1VYgy2+RGAlphG2H4lkAdCghAdViUvC1J6ytvFbWfagS0wqpRMoZAdgICUHb2hZhZ66sQZbbJLgS0wrqAcTeBOhAQgOqgCHlegtZXnqtrb9UIaIVVo2QMgfQFBKD0zQszo9ZXYUpto90IlFthhx56aHiDxG6QPEQgAwEBKAP0Ikyp9VWEKttjtQITJ070BonVYhlHICUBASgl6KJNo/VVtIrbb08CWmE9CXmcQLoCAlC63oWYTeurEGW2yYQCWmEJwQwn0M8CAlA/Axft9FpfRau4/SYR0ApLomUsgf4VEID617dwZ9f6KlzJbTihgFZYQjDDCfSTgADUT7BFPK3WVxGrbs9JBbTCkooZT6B/BASg/nEt3Fm1vgpXchvug8CGVthdd93Vh7M4lACBvggIQH3Rc+xLAlpfL1H4hkBVAuVW2AMPPBBLliyparxBBAjUVkAAqq1nIc+m9VXIstt0HwW0wvoI6HACfRQQgPoIWPTDtb6KfgXYf18EtML6oudYAn0TEID65lf4o7W+Cn8JAOijgFZYHwEdTqCXAgJQL+EcFqH15Sog0HcBrbC+GzoDgd4ICEC9UcvhMQ8//HB89oIL4rgjD42dt98qhg4ZFAMHNsfmo4bHAftMirPf90/x61//Otrb29fvXusrhxeBLWUmsKlWWPkHjCuuuCLecvrJseeuO8WoEUOjuXlAjB45NCZN3CFOf8NJ8d3vfjeWLVuW2bpNTKCRBZpK/yPrbOQNWHvfBO6+++74z499NO6ac2ectG/EoRNbY/dtm2KrkU0xeGDEynURjy7pjDsf7YjpD46Ip54fEOf+x6dicunTrcu/vXLiiSdGU1NT3xbhaAIEYs2aNXHVVVfF/vvvH5d9+xvxgx/+IKbuMTgOn7AqXrHTgNh5y6YYMaQpVq3rjKeWdcb9T3fG7EeHxa3z1sWb3vzm+NR5F8SOO+5IkgCBKgUEoCqh8jaso6MjPv6xf4vvX/bt+MixrXHawc0xsLnnIDN/YUdcfMuQeHz5qLjuxumx55575o3GfghkJvCTn/wk/vn9/xQn798Z7z+iPbYo/SDS09fyNZ3xnVkD4qo5zfHty/47Tj311J4O8TgBAiUBAaiAl0Fra2uc8roTYtnjf4xvvrU1Nhve8z+ylUw/+WNnfPyn6+Ivf/lLTJo0qfJhtwkQSChwycUXxhc/++n43jvb4xU7Jn91wkOLO+L4L7XEFz5/QZz7sU8knN1wAsUTEIAKVvPyMz+7TdwlJo5eGt88szOaByQPPxvIpv+5Pd5zeWvMmTMnDjjggA13+5MAgYQCv/rlL+LNb35j/OacQbHDFr3/O7lkeWeceElH/Mu/fjw++anzEq7CcALFEkj+Y0axfHK323/54Nmx5sVFfQ4/ZZhj9m6Ob5w1KF534nGxePHi3FnZEIE0BG699dY4/U1vjJv+bWCfwk95rduMboobPjogvvXVC+Oaa65JY/nmINCwAp4BatjSJV/4zTffHO8569T49UfaYujg3v+UWTnz12dEPNgxNa694RYviK7EcZtANwIvvvhi7DVpQnzl9JVxwPja/Txafq3eWZcPiT/NfyjGjh3bzQo8RKC4ArX7G1dcw4bYefmX/f79Ix+IT57UUnX4WdvSXNXe3n9kZ9x39x/itttuq2q8QQQI/FXg0osviikTW6oKP2taBsbC50dWRbfn9gPi9fu1x+cv+K+qxhtEoIgCngEqSNVnzJgRH/qnN8T1H2rpccdPPDc6vj3joFjw7OZx5dk/73F8ecBPbm+PP646Mq65/uaqxhtEoOgCLS0tsf22W8XV718X47bq+mfRtvam+OTVR8eTy0bHfU+MjV23XRbfesf1MXazVd0SLl3RGcdd2BRPLlwSo0aN6nasBwkUUaDrv3VF1Mjxnq/80RVx6r5retzhqnWDSu8zMjgWvTAy1rVW9wxQ+aSv339A3Drzt7F8+fIe5zCAAIGI6dOnx27bNncbfspOM/48IT543O3xo/f/Iv74X5fF06Ug9IPZpTft6uFr61FNccjuQ+P666/vYaSHCRRTQAAqSN1vnf6bOHxSz6/7GTGkNfbc/tnYYcyK0ut5qscZVnpN0b4ThmuDVU9mZMEFZs64JaaW3uSwp6/Juz0Z249ZuX7YsMFt8Q87PRO7bP1CT4etf3zq+JUx/WYBqCosgwonIAAVoOSrV6+Ohc88FxO2SZBoeuGy+9ZrYt68eb040iEEiidw791/jD2263nfo4f9rW396JIxMXxIS5xy4PyeDyyN2KP0WqD77r27qrEGESiagABUgIovXLgwdtxmRL//htaOm7fFU088VgBRWyTQd4H1fy+rfM+flrYB8Z1bD4jXX3pGPLR4y1iwdPOqFrDDmKbSW1QsqWqsQQSKJiAAFaDiq1atKv3U2P+lHjooYvWqFQUQtUUCfRdYtXptDB9c3XnaOwaUflNsYXzgmDtKn803Js7+7xOjvaPnZ3TL51+1pvSBfr4IEPg7gdLHXfrKu8DIkSNj5Zq/fop7f+51demZ+pFjN+vPKZybQG4ERo4YHivWVvdanvJrfw4cv2j9f2M3Wxn/euWr47GlY2LXsd1/Enz5w4xHDB+aGzMbIVBLgf5/WqCWq3WuXgmMGzcuFj67OtraO3t1fLUHLVg2OCbutke1w40jUGiB8RPGx+PPJv87efTej5Xa2Z3RUcWhjz/bEePH7VRoZ5sn0JWAANSVTI7uHzhwYEyauHPMe7KKfzH7sO97nxoU++23Xx/O4FACxRE44ODD4p4nqn+riQ0y5TdE3Hz42pi4zfMb7uryz7mPRxxw8JQuH/cAgSILCEAFqf7xr3ld/PbB6su9pnVg6TUG1Y8vv+naE8+2xsEHH1wQUdsk0DeB445/dfzukZ5fBHTLvImxdMXwlya78d7d4pMnzyp9kHHPP9DMfnREHH/CSS8d6xsCBP4mUP3/4f52jO8aUODtZ70rrp7TXAo13f+jua6tOX5+554x+y/j4s9PbR2/nLNHvLBqSI87vuqOiNNOOzUGD+75H/QeT2YAgQIIvOpVr4rWGBH3PN7R7W7/5w+viMM+86746I+Pjyt+t+/61/2cfMAD3R5TfvDhZzriiecijj766B7HGkCgiAI+CqNAVT/uqMNi2lZ3xFsm1zb3rljbGUd9cUDc9se5MWnSpAKJ2iqBvgl85zvfiSu/fk7897v+9l4/mzrjirWDY+igthjU3H1Y2vjYD145KA4/7eNxzrkf2/hu3xMg8H8Ctf0/Ida6Frjw0q/Hl38zMBa/0P2zQEk3cf51g+K0088QfpLCGV94gXe+852xrL30TOtd3QebUUNbEoWf6X9qj/uXjIwP/PMHC28MgEBXAgJQVzI5vH+fffaJCz5/UZz5vYGxfE1tQtC7L2+Lx9bsHF/+6jdyKGZLBPpXoNwy/sWvborP3TA4/vBQbd6qYu6CjnjP91vjl9feFCNGjOjfDTg7gQYWEIAauHi9Wfp73/f+OO51Z8QJF3fGcyv7FoK+MaOj9EGNbTHzd7fH0KHea6Q39XAMgd122y3+52e/iLd+qzVmP9C3EFQOP6d+tSVmz54d5R94fBEg0LWAANS1TW4f+ca3LosPfOTj8fqvDii90Ln7p943hbCutTO+//uhcfOjO8WiRYti1KhRmxrmPgIEqhQ49thjY/78+fEvpdftXHVH8r+T5Wmun9se7/7BwLjuuuti6tSpVc5sGIHiCngRdHFrH1dddVWc/b5/jOP26oj3TmuNnbbsPg+va+uMX93VHt+YOSQmH3ZMXPHDK2P48L/9em6BKW2dQE0EHnnkkTjtlBNjSOui+OCRa+LQ3Xp+n6C7HuuIr80cGs+2bhk/+/l1sffee9dkLU5CIO8CAlDeK9zD/p5//vm49OIL41vf+nrpjdWaY/L4NbH72I7YYmRT6bdOIlaUXiv06JLOmPPksPjd/JaY/KpD4j/P+1xMmeLN1Xqg9TCBXgm0tbXFlVdeGRd+/r9i+fNLYtqkzth7u3UxrvQDyohSp3l16eMtnlzWGfcvGhS/e6g5modsFv967ifjHe94h7eh6JW4g4oqIAAVtfIV+25tbY2ZM2fGrJkzYv68u+OZZxZHS+m+cntrt933ikMPOzJOOOGEGDt2bMWRbhIg0F8C8+bNi9/85jcx987bYsGCR2LRwkWx3XbbxU477xL7HXRo6T1+jon999+/v6Z3XgK5FhCAcl1emyNAIE8C3/ve96L8q/PNzT23xvK0b3sh0B8C3b/ooz9mdE4CBAgQIECAQMYCAlDGBTA9AQIECBAgkL6AAJS+uRkJECBAgACBjAUEoIwLYHoCBAgQIEAgfQEBKH1zMxIgQIAAAQIZCwhAGRfA9AQIECBAgED6AgJQ+uZmJECAAAECBDIWEIAyLoDpCRAgQIAAgfQFBKD0zc1IgAABAgQIZCwgAGVcANMTIECAAAEC6QsIQOmbm5EAAQIECBDIWEAAyrgApidAgAABAgTSFxCA0jc3IwECBAgQIJCxgACUcQFMT4AAAQIECKQvIAClb25GAgQIECBAIGMBASjjApieAAECBAgQSF9AAErf3IwECBAgQIBAxgICUMYFMD0BAgQIECCQvoAAlL65GQkQIECAAIGMBQSgjAtgegIECBAgQCB9AQEofXMzEiBAgAABAhkLCEAZF8D0BAgQIECAQPoCAlD65mYkQIAAAQIEMhYQgDIugOkJECBAgACB9AUEoPTNzUiAAAECBAhkLCAAZVwA0xMgQIAAAQLpCwhA6ZubkQABAgQIEMhYQADKuACmJ0CAAAECBNIXEIDSNzcjAQIECBAgkLGAAJRxAUxPgAABAgQIpC8gAKVvbkYCBAgQIEAgYwEBKOMCmJ4AAQIECBBIX0AASt/cjAQIECBAgEDGAgJQxgUwPQECBAgQIJC+gACUvrkZCRAgQIAAgYwFBKCMC2B6AgQIECBAIH0BASh9czMSIECAAAECGQsIQBkXwPQECBAgQIBA+gICUPrmZiRAgAABAgQyFhCAMi6A6QkQIECAAIH0BQSg9M3NSIAAAQIECGQsIABlXADTEyBAgAABAukLCEDpm5uRAAECBAgQyFhAAMq4AKYnQIAAAQIE0hcQgNI3NyMBAgQIECCQsYAAlHEBTE+AAAECBAikLyAApW9uRgIECBAgQCBjAQEo4wKYngABAgQIEEhfQABK39yMBAgQIECAQMYCAlDGBTA9AQIECBAgkL6AAJS+uRkJECBAgACBjAUEoIwLYHoCBAgQIEAgfQEBKH1zMxIgQIAAAQIZCwhAGRfA9AQIECBAgED6AgJQ+uZmJECAAAECBDIWEIAyLoDpCRAgQIAAgfQFBKD0zc1IgAABAgQIZCwgAGVcANMTIECAAAEC6QsIQOmbm5EAAQIECBDIWEAAyrgApidAgAABAgTSFxCA0jc3IwECBAgQIJCxgACUcQFMT4AAAQIECKQvIAClb25GAgQIECBAIGMBASjjApieAAECBAgQSF9AAErf3IwECBAgQIBAxgICUMYFMD0BAgQIECCQvoAAlL65GQkQIECAAIGMBQSgjAtgegIECBAgQCB9AQEofXMzEiBAgAABAhkLCEAZF8D0BAgQIECAQPoCAlD65mYkQIAAAQIEMhYQgDIugOkJECBAgACB9AUEoPTNzUiAAAECBAhkLCAAZVwA0xMgQIAAAQLpCwhA6ZubkQABAgQIEMhYQADKuACmJ0CAAAECBNIXEIDSNzcjAQIECBAgkLGAAJRxAUxPgAABAgQIpC8gAKVvbkYCBAgQIEAgYwEBKOMCmJ4AAQIECBBIX0AASt/cjAQIECBAgEDGAgJQxgUwPQECBAgQIJC+gACUvrkZCRAgQIAAgYwFBKCMC2B6AgQIECBAIH0BASh9czMSIECAAAECGQsIQBkXwPQECBAgQIBA+gICUPrmZiRAgAABAgQyFhCAMi6A6QkQIECAAIH0BQSg9M3NSIAAAQIECGQsIABlXADTEyBAgAABAukLCEDpm5uRAAECBAgQyFhAAMq4AKYnQIAAAQIE0hcQgNI3NyMBAgQIECCQsYAAlHEBTE+AAAECBAikLyAApW9uRgIECBAgQCBjAQEo4wKYngABAgQIEEhfQABK39yMBAgQIECAQMYCAlDGBTA9AQIECBAgkL6AAJS+uRkJECBAgACBjAUEoIwLYHoCBAgQIEAgfQEBKH1zMxIgQIAAAQIZCwhAGRfA9AQIECBAgED6AgJQ+uZmJECAAAECBDIWEIAyLoDpCRAgQIAAgfQFBKD0zc1IgAABAgQIZCwgAGVcANMTIECAAAEC6QsIQOmbm5EAAQIECBDIWEAAyrgApidAgAABAgTSFxCA0jc3IwECBAgQIJCxgACUcQFMT4AAAQIECKQvIAClb25GAgQIECBAIGMBASjjApieAAECBAgQSF9AAErf3IwECBAgQIBAxgICUMYFMD0BAgQIECCQvoAAlL65GQkQIECAAIGMBQSgjAtgegIECBAgQCB9AQEofXMzEiBAgAABAhkLCEAZF8D0BAgQIECAQPoCAlD65mYkQIAAAQIEMhYQgDIugOkJECBAgACB9AUEoPTNzUiAAAECBAhkLCAAZVwA0xMgQIAAAQLpCwhA6ZubkQABAgQIEMhYQADKuACmJ0CAAAECBNIXEIDSNzcjAQIECBAgkLGAAJRxAUxPgAABAgQIpC8gAKVvbkYCBAgQIEAgYwEBKOMCmJ4AAQIECBBIX0AASt/cjAQIECBAgEDGAgJQxgUwPQECBAgQIJC+gACUvrkZCRAgQIAAgYwFBKCMC2B6AgQIECBAIH0BASh9czMSIECAAAECGQsIQBkXwPQECBAgQIBA+gICUPrmZiRAgAABAgQyFhCAMi6A6QkQIECAAIH0BQSg9M3NSIAAAQIECGQsIABlXADTEyBAgAABAukLCEDpm5uRAAECBAgQyFhAAMq4AKYnQIAAAQIE0hcQgNI3NyMBAgQIECCQsYAAlHEBTE+AAAECBAikLyAApW9uRgIECBAgQCBjAQEo4wKYngABAtUKdHR0VDvUOAIEehBo6ix99TDGwwQIECCQssALL7wQV199dUz/9bUxd+5d8eTTS2Nda1sMHTwothu7Reyzzz5x7Aknx2mnnRZbbbVVyqszHYHGFxCAGr+GdkCAQI4EFi9eHJ8575Px4x//KKbtNTgOn7gmXrFjU4zbsqkUfppKIagznlzWGX9+qjNmPzI0ps9riTeefnp8+r8+GzvttFOOJGyFQP8KCED96+vsBAgQqFrgxhtvjLPe9uY4Zf/2eN8R7TFmRFOPxy5f0xnf+92A+J8/Dohvfuf7cXopDPkiQKBnAQGoZyMjCBAg0O8CF37xC3Hxl86Py9/ZFnvtkPzlmY880xHHfrElLjj/vPjEf36639drAgKNLiAANXoFrZ8AgYYX+MXVV8Xb3nZG3HLOwNhhTM/P+nS14aXLO+OkSzviAx8+Nz593vldDXM/AQIlAQHIZUCAAIEMBaZPnx4nvebVpfDTHDttmfyZn8qll0PQyV8bEN+47MdxyimnVD7sNgEC/ycgALkUCBAgkJFA+Te99t5jYnztTStjv136Hn42bOOBRR1x5uVD4r4/Pxjbbrvthrv9SYDARgK1+xu30Ul9S4AAAQI9C1x6yUVx2K4ticLP86uGxtqW5m5PPmm7AfH6fdvjC5/9TLfjPEigyAKeASpy9e2dAIHMBFpaWmKH7baKn79/XdWtr+VrBsdrLnxbXHjGLTF5t6e6XfuzKzrj2IsGxFMLl8TIkSO7HetBAkUU8AxQEatuzwQIZC5Qfu3Pbtsme93P93+7fyx8YXRVa99qVFMcstuQuPbaa6sabxCBogkIQEWruP0SIFAXAjN+8+uYOn5V1Wu58Z7dYuqkx6seXx44dfzKmH7zDYmOMZhAUQQEoKJU2j4JEKgrgXvn3hF7bFfdkha9MDKeeG6zOHD8ouoO+L9Re2w/IObdd3eiYwwmUBQBAagolbZPAgTqSmDxokWxwxY9v+dP+dMaL5t5QLxrWvIgU35PocWLl9TVvi2GQL0ICED1UgnrIECgUAKr16yNYYN63vIPZu8bpx18fwwemPyT4MvnX712Xc+TGEGggAICUAGLbssECGQvMGL48FjVQzaZv3CrWLlucEwcuyzWtTWv/6+88raOAdHS1vM/3+Xzjxw+LPvNWgGBOhQYWIdrsiQCBAjkXmDixAmx4NmnY4/tu97qjD9NiEt+fWhcctOhLxt01nfeENtutiJ+/+nLX3Z/5Y0Fz3bE+F12rrzbbQIESgICkMuAAAECGQjsf/BhMfd3v49X/0PXk7/j8Llxaqn9tfHXYee/K77y9pvi4AlPb3z3Jr+f+0TE/ge9PDxtcqA7CRRQoOfnUAuIYssECBDob4Hjjn91/O6hwd1OM3Joa2y3+cqX/Vc+YIsRa2Lr0au7Pbb84P8+OiKOP+GkHscZQKCIAgJQEatuzwQIZC5wyCGHRNuAkTF3QfIXN1ez+IcWd8STy5riqKOOqma4MQQKJyAAFa7kNkyAQD0INDU1xTkf/3RcOmNoouU8fPFXe/wYjPIJvzJjSHz4o+fEoEFV/KpZohUYTCAfAj4LLB91tAsCBBpQoPx5YAfut3e8Y98n4pQDa/fz6G/+1B5fnLH1+k+DHzFiRAPKWDKB/hfwIuj+NzYDAQIENikwePDg+MWvborJB+8X24xeF1N27/5T3jd5koo773qsI977/da4775fh/BTgeMmgY0Eavcjx0Yn9S0BAgQIVCew6667xk+v/lW8/dutMWt+e3UHdTGqHH7e+LWW+P3vfx+vfOUruxjlbgIEygICkOuAAAECGQuUX6j84IMPxjlXD42f3N67F0Vfd3d7vPeHg+KGG26IyZMnZ7wj0xOofwGvAar/GlkhAQIFEXj00Ufj9FNfGwPWPBUfPHJNTK2iJXbHIx3x9VlD48XObUrPJF0be+65Z0G0bJNA3wQEoL75OZoAAQI1FWhra4uf/vSn8aXPnRfLnl0Uh+8e8Yrt1sW4rZpi+JCmWNPSWfpk+M64f/GQmP3QgBgyfEz8+398Ot72trdF+TVFvggQqE5AAKrOySgCBAikLnD//ffHjBkz4q47Zsdjjz4cq1evifJniO208y6x30FT1r/Hz7777pv6ukxIIA8CAlAeqmgPBAgQIECAQCIBL4JOxGUwAQIECBAgkAcBASgPVbQHAgQIECBAIJGAAJSIy2ACBAgQIEAgDwICUB6qaA8ECBAgQIBAIgEBKBGXwQQIECBAgEAeBASgPFTRHggQIECAAIFEAgJQIi6DCRAgQIAAgTwICEB5qKI9ECBAgAABAokEBKBEXAYTIECAAAECeRAQgPJQRXsgQIAAAQIEEgkIQIm4DCZAgAABAgTyICAA5aGK9kCAAAECBAgkEhCAEnEZTIAAAQIECORBQADKQxXtgQABAgQIEEgkIAAl4jKYAAECBAgQyIOAAJSHKtoDAQIECBAgkEhAAErEZTABAgQIECCQBwEBKA9VtAcCBAgQIEAgkYAAlIjLYAIECBAgQCAPAgJQHqpoDwQIECBAgEAiAQEoEZfBBAgQIECAQB4EBKA8VNEeCBAgQIAAgUQCAlAiLoMJECBAgACBPAgIQHmooj0QIECAAAECiQQEoERcBhMgQIAAAQJ5EBCA8lBFeyBAgAABAgQSCQhAibgMJkCAAAECBPIgIADloYr2QIAAAQIECCQSEIAScRlMgAABAgQI5EFAAMpDFe2BAAECBAgQSCQgACXiMpgAAQIECBDIg4AAlIcq2gMBAgQIECCQSEAASsRlMAECBAgQIJAHAQEoD1W0BwIECBAgQCCRgACUiMtgAgQIECBAIA8CAlAeqmgPBAgQIECAQCIBASgRl8EECBAgQIBAHgQEoDxU0R4IECBAgACBRAICUCIugwkQIECAAIE8CAhAeaiiPRAgQIAAAQKJBASgRFwGEyBAgAABAnkQEIDyUEV7IECAAAECBBIJCECJuAwmQIAAAQIE8iAgAOWhivZAgAABAgQIJBIQgBJxGUyAAAECBAjkQUAAykMV7YEAAQIECBBIJCAAJeIymAABAgQIEMiDgACUhyraAwECBAgQIJBIQABKxGUwAQIECBAgkAcBASgPVbQHAgQIECBAIJGAAJSIy2ACBAgQIEAgDwICUB6qaA8ECBAgQIBAIgEBKBGXwQQIECBAgEAeBASgPFTRHggQIECAAIFEAgJQIi6DCRAgQIAAgTwICEB5qKI9ECBAgAABAokEBKBEXAYTIECAAAECeRAQgPJQRXsgQIAAAQIEEgkIQIm4DCZAgAABAgTyICAA5aGK9kCAAAECBAgkEhCAEnEZTIAAAQIECORBQADKQxXtgQABAgQIEEgkIAAl4jKYAAECBAgQyIOAAJSHKtoDAQIECBAgkEhAAErEZTABAgQIECCQBwEBKA9VtAcCBAgQIEAgkYAAlIjLYAIECBAgQCAPAgJQHqpoDwQIECBAgEAiAQEoEZfBBAgQIECAQB4EBKA8VNEeCBAgQIAAgUQCAlAiLoMJECBAgACBPAgIQHmooj0QIECAAAECiQQEoERcBhMgQIAAAQJ5EBCA8lBFeyBAgAABAgQSCQhAibgMJkCAAAECBPIgIADloYr2QIAAAQIECCQSEIAScRlMgAABAgQI5EFAAMpDFe2BAAECBAgQSCQgACXiMpgAAQIECBDIg4AAlIcq2gMBAgQIECCQSEAASsRlMAECBAgQIJAHAQEoD1W0BwIECBAgQCCRgACUiMtgAgQIECBAIA8CAlAeqmgPBAgQIECAQCIBASgRl8EECBAgQIBAHgQEoDxU0R4IECBAgACBRAICUCIugwkQIECAAIE8CAhAeaiiPRAgQIAAAQKJBASgRFwGEyBAgAABAnkQEIDyUEV7IECAAAECBBIJCECJuAwmQIAAAQIE8iAgAOWhivZAgAABAgQIJBIQgBJxGUyAAAECBAjkQUAAykMV7YEAAQIECBBIJCAAJeIymAABAgQIEMiDgACUhyraAwECBAgQIJBIQABKxGUwAQIECBAgkAcBASgPVbQHAgQIECBAIJGAAJSIy2ACBAgQIEAgDwICUB6qaA8ECBAgQIBAIgEBKBGXwQQIECBAgEAeBASgPFTRHggQIECAAIFEAgJQIi6DCRAgQIAAgTwICEB5qKI9ECBAgAABAokEBKBEXAYTIECAAAECeRAQgPJQRXsgQIAAAQIEEgkIQIm4DCZAgAABAgTyICAA5aGK9kCAAAECBAgkEhCAEnEZTIAAAQIECORBQADKQxXtgQABAgQIEEgkIAAl4jKYAAECBAgQyIPA/wcH6NMs7KdskwAAAABJRU5ErkJggg==" /><!-- --></p>
<p>As another example, let’s make a chordal graph. Remember that a graph
is chordal (or triangulated) if each of its cycles of four or more nodes
has a chord, which is an edge joining two nodes that are not adjacent in
the cycle. First, let’s create the initial graph using
<code>graph_from_literal()</code>:</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>g1 <span class="ot">&lt;-</span> <span class="fu">graph_from_literal</span>(</span>
<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a>  A <span class="sc">-</span> B<span class="sc">:</span>C<span class="sc">:</span>I, B <span class="sc">-</span> A<span class="sc">:</span>C<span class="sc">:</span>D, </span>
<span id="cb27-3"><a href="#cb27-3" 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="cb27-4"><a href="#cb27-4" tabindex="-1"></a>  D <span class="sc">-</span> B<span class="sc">:</span>E<span class="sc">:</span>F,</span>
<span id="cb27-5"><a href="#cb27-5" 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="cb27-6"><a href="#cb27-6" tabindex="-1"></a>  F <span class="sc">-</span> D<span class="sc">:</span>E<span class="sc">:</span>G, </span>
<span id="cb27-7"><a href="#cb27-7" tabindex="-1"></a>  G <span class="sc">-</span> F<span class="sc">:</span>H, </span>
<span id="cb27-8"><a href="#cb27-8" 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="cb27-9"><a href="#cb27-9" tabindex="-1"></a>  I <span class="sc">-</span> A<span class="sc">:</span>H</span>
<span id="cb27-10"><a href="#cb27-10" tabindex="-1"></a>)</span>
<span id="cb27-11"><a href="#cb27-11" 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>In the example above, the <code>:</code> operator was used to define
vertex sets. If an edge operator connects two vertex sets, then every
vertex from the first set will be connected to every vertex in the
second set. Then we use <code>is_chordal()</code> to evaluate if our
graph is chordal and to search what edges are missing to fill-in the
graph:</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><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
## 
## $newgraph
## NULL</code></pre>
<p>We can then add the edges required to make the initial graph chordal
in a single line:</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a>chordal_graph <span class="ot">&lt;-</span> <span class="fu">add_edges</span>(g1, <span class="fu">is_chordal</span>(g1, <span class="at">fillin =</span> <span class="cn">TRUE</span>)<span class="sc">$</span>fillin)</span>
<span id="cb30-2"><a href="#cb30-2" 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="constructing-graphs" class="section level2">
<h2>Constructing graphs</h2>
<p>In addition to <code>make_empty_graph()</code>,
<code>make_graph()</code>, and <code>make_graph_from_literal()</code>,
<code>igraph</code> includes many other function to construct a graph.
Some are <em>deterministic</em>, that is to say they produce the same
graph each single time, for instance <code>make_tree()</code>:</p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" 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="cb31-2"><a href="#cb31-2" tabindex="-1"></a><span class="fu">summary</span>(graph1)</span></code></pre></div>
<pre><code>## IGRAPH daa103a U--- 127 126 -- Tree
## + attr: name (g/c), children (g/n), mode (g/c)</code></pre>
<p>This generates a regular tree graph with 127 vertices, each vertex
having two children. No matter how many times you call
<code>make_tree()</code>, the generated graph will always be the same if
you use the same parameters:</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>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="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" tabindex="-1"></a><span class="fu">identical_graphs</span>(graph1, graph2)</span></code></pre></div>
<pre><code>## [1] TRUE</code></pre>
<p>Other functions generate graphs <em>stochastically</em>, which means
they produce a different graph each time. For instance
<code>sample_grg()</code>:</p>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" 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="cb36-2"><a href="#cb36-2" tabindex="-1"></a><span class="fu">summary</span>(graph1)</span></code></pre></div>
<pre><code>## IGRAPH bad61bd U--- 100 546 -- Geometric random graph
## + attr: name (g/c), radius (g/n), torus (g/l)</code></pre>
<p>This generates a geometric random graph: <em>n</em> points are chosen
randomly and uniformly inside the unit square and pairs of points closer
to each other than a predefined distance <em>d</em> are connected by an
edge. If you generate GRGs with the same parameters, they will be
different:</p>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" 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="cb38-2"><a href="#cb38-2" tabindex="-1"></a><span class="fu">identical_graphs</span>(graph1, graph2)</span></code></pre></div>
<pre><code>## [1] FALSE</code></pre>
<p>A slightly looser way to check if the graphs are equivalent is via
<code>isomorphic</code>. Two graphs are said to be isomorphic if they
have the same number of components (vertices and edges) and maintain a
one-to-one correspondence between vertices and edges, that is to say,
they are connected in the same way.</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">isomorphic</span>(graph1, graph2)</span></code></pre></div>
<pre><code>## [1] FALSE</code></pre>
<p>Checking for isomorphism can take a while for large graphs (in this
case, the answer can quickly be given by checking the degree sequence of
the two graphs). <code>identical_graph()</code> is a stricter criterion
than <code>isomorphic()</code>: the two graphs must have the same list
of vertices and edges, in exactly the same order, with same
directedness, and the two graphs must also have identical graph, vertex
and edge attributes.</p>
</div>
<div id="setting-and-retrieving-attributes" class="section level2">
<h2>Setting and retrieving attributes</h2>
<p>In addition to IDs, vertex and edges can have <em>attributes</em>
such as a name, coordinates for plotting, metadata, and weights. The
graph itself can have such attributes too (for instance a name, which
will show in <code>summary()</code>). In a sense, every graph, vertex
and edge can be used as an R namespace to store and retrieve these
attributes.</p>
<p>To demonstrate the use of attributes, let us create a simple social
network:</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, Himari <span class="sc">-</span> Alice<span class="sc">:</span>Nang<span class="sc">:</span>Moshe<span class="sc">:</span>Samira,</span>
<span id="cb42-3"><a href="#cb42-3" tabindex="-1"></a>  Ibrahim <span class="sc">-</span> Nang<span class="sc">:</span>Moshe, Nang <span class="sc">-</span> Samira</span>
<span id="cb42-4"><a href="#cb42-4" tabindex="-1"></a>)</span></code></pre></div>
<p>Each vertex represents a person, so we want to store ages, genders
and types of connection between two people (<code>is_formal()</code>
refers to whether a connection between one person or another is formal
or informal, respectively colleagues or friends). The <code>$</code>
operator is a shortcut to get and set graph attributes. It is shorter
and just as readable as <code>graph_attr()</code> and
<code>set_graph_attr()</code>.</p>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</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="cb43-2"><a href="#cb43-2" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;m&quot;</span>, <span class="st">&quot;f&quot;</span>, <span class="st">&quot;m&quot;</span>)</span>
<span id="cb43-3"><a href="#cb43-3" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</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="cb43-4"><a href="#cb43-4" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div>
<pre><code>## IGRAPH 64d92f3 UN-- 7 9 -- 
## + attr: name (v/c), age (v/n), gender (v/c), is_formal (e/l)</code></pre>
<p><code>V()</code> and <code>E()</code> are the standard way to obtain
a sequence of all vertices and edges, respectively. This assigns an
attribute to <em>all</em> vertices/edges at once. Another way to
generate our social network is with the use of
<code>set_vertex_attr()</code> and <code>set_edge_attr()</code> and the
operator <code>%&gt;%</code>:</p>
<div class="sourceCode" id="cb45"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">make_graph</span>(</span>
<span id="cb45-2"><a href="#cb45-2" tabindex="-1"></a>  <span class="sc">~</span> Alice <span class="sc">-</span> Boris<span class="sc">:</span>Himari<span class="sc">:</span>Moshe, Himari <span class="sc">-</span> Alice<span class="sc">:</span>Nang<span class="sc">:</span>Moshe<span class="sc">:</span>Samira,</span>
<span id="cb45-3"><a href="#cb45-3" tabindex="-1"></a>  Ibrahim <span class="sc">-</span> Nang<span class="sc">:</span>Moshe, Nang <span class="sc">-</span> Samira</span>
<span id="cb45-4"><a href="#cb45-4" tabindex="-1"></a>) <span class="sc">%&gt;%</span></span>
<span id="cb45-5"><a href="#cb45-5" 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="cb45-6"><a href="#cb45-6" 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="cb45-7"><a href="#cb45-7" 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="cb45-8"><a href="#cb45-8" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div>
<p>To assign or modify an attribute for a single vertex/edge:</p>
<div class="sourceCode" id="cb46"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal</span></code></pre></div>
<pre><code>## [1] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE</code></pre>
<div class="sourceCode" id="cb48"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal[<span class="dv">1</span>] <span class="ot">&lt;-</span> <span class="cn">TRUE</span></span>
<span id="cb48-2"><a href="#cb48-2" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal</span></code></pre></div>
<pre><code>## [1]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE</code></pre>
<p>Attribute values can be set to any R object, but note that storing
the graph in some file formats might result in the loss of complex
attribute values. Vertices, edges and the graph itself can all be used
to set attributes, for instance to add a date to the graph:</p>
<div class="sourceCode" id="cb50"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb50-1"><a href="#cb50-1" tabindex="-1"></a>g<span class="sc">$</span>date <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;2022-02-11&quot;</span>)</span>
<span id="cb50-2"><a href="#cb50-2" 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>To retrieve attributes, you can also use <code>graph_attr()</code>,
<code>vertex_attr()</code>, and <code>edge_attr()</code>. To find the ID
of a vertex you can use the function <code>match()</code>:</p>
<div class="sourceCode" id="cb52"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb52-1"><a href="#cb52-1" 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">$</span>name)</span></code></pre></div>
<pre><code>## [1] 7</code></pre>
<p>To assign attributes to a subset of vertices or edges, you can
use:</p>
<div class="sourceCode" id="cb54"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</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="cb54-2"><a href="#cb54-2" tabindex="-1"></a><span class="fu">V</span>(g)</span></code></pre></div>
<pre><code>## + 7/7 vertices, named, from 64d92f3:
## [1] Alejandra Bruno     Carmina   Moshe     Nang      Samira    Ibrahim</code></pre>
<p>To delete attributes:</p>
<div class="sourceCode" id="cb56"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb56-1"><a href="#cb56-1" 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="cb56-2"><a href="#cb56-2" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>gender</span></code></pre></div>
<pre><code>## NULL</code></pre>
<p>If you want to save a graph in R with all the attributes use
<code>saveRDS()</code> (and then <code>readRDS()</code> to retrieve
it).</p>
</div>
<div id="structural-properties-of-graphs" class="section level2">
<h2>Structural properties of graphs</h2>
<p><code>igraph</code> provides a large set of functions to calculate
various structural properties of graphs. It is beyond the scope of this
tutorial to document all of them, hence this section will only introduce
a few of them for illustrative purposes. We will work on the small
social network constructed in the previous section.</p>
<p>Perhaps the simplest property one can think of is the
<em>degree</em>. The degree of a vertex equals the number of edges
adjacent to it. In case of directed networks, we can also define
<em>in-degree</em> (the number of edges pointing towards the vertex) and
<em>out-degree</em> (the number of edges originating from the vertex).
<code>igraph</code> is able to calculate all of them using a simple
syntax:</p>
<div class="sourceCode" id="cb58"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb58-1"><a href="#cb58-1" 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>If the graph was directed, we would have been able to calculate the
in- and out-degrees separately using <code>degree(mode = &quot;in&quot;)</code>
and <code>degree(mode = &quot;out&quot;)</code>. You can also pass a single vertex
ID or a list of vertex IDs to <code>degree()</code> if you want to
calculate the degrees for only a subset of vertices:</p>
<div class="sourceCode" id="cb60"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb60-1"><a href="#cb60-1" 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="cb62"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb62-1"><a href="#cb62-1" 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>Most functions that accept vertex IDs also accept vertex
<em>names</em> (the values of the <code>name</code> vertex attribute) as
long as the names are unique:</p>
<div class="sourceCode" id="cb64"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb64-1"><a href="#cb64-1" 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>It also works for single vertices:</p>
<div class="sourceCode" id="cb66"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb66-1"><a href="#cb66-1" 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>A similar syntax is used for most of the structural properties
<code>igraph</code> can calculate. For vertex properties, the functions
accept a vertex ID, a vertex name, or a list of vertex IDs or names (and
if they are omitted, the default is the set of all vertices). For edge
properties, the functions accept a single edge ID or a list of edge
IDs.</p>
<hr />
<p><strong>NOTE:</strong> For some measures, it does not make sense to
calculate them only for a few vertices or edges instead of the whole
graph, as it would take the same time anyway. In this case, the
functions won’t accept vertex or edge IDs, but you can still restrict
the resulting list later using standard operations. One such example is
eigenvector centrality (<code>evcent()</code>).</p>
<hr />
<p>Besides degree, igraph includes built-in routines to calculate many
other centrality properties, including vertex and edge betweenness
(<code>edge_betweenness()</code>) or Google’s PageRank
(<code>page_rank()</code>) just to name a few. Here we just illustrate
edge betweenness:</p>
<div class="sourceCode" id="cb68"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb68-1"><a href="#cb68-1" 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>Now we can also figure out which connections have the highest
betweenness centrality:</p>
<div class="sourceCode" id="cb70"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb70-1"><a href="#cb70-1" tabindex="-1"></a>ebs <span class="ot">&lt;-</span> <span class="fu">edge_betweenness</span>(g)</span>
<span id="cb70-2"><a href="#cb70-2" 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="querying-vertices-and-edges-based-on-attributes" class="section level2">
<h2>Querying vertices and edges based on attributes</h2>
<div id="selecting-vertices" class="section level3">
<h3>Selecting vertices</h3>
<p>Imagine that in a given social network, you want to find out who has
the largest degree. You can do that with the tools presented so far and
the <code>which.max()</code> function:</p>
<div class="sourceCode" id="cb72"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb72-1"><a href="#cb72-1" 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>Another example would be to select only vertices that have only odd
IDs but not even ones, using the <code>V()</code> function:</p>
<div class="sourceCode" id="cb74"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb74-1"><a href="#cb74-1" 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></code></pre></div>
<pre><code>## Warning: `graph.full()` was deprecated in igraph 2.1.0.
## ℹ Please use `make_full_graph()` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.</code></pre>
<div class="sourceCode" id="cb76"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb76-1"><a href="#cb76-1" 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="cb76-2"><a href="#cb76-2" tabindex="-1"></a><span class="fu">length</span>(only_odd_vertices)</span></code></pre></div>
<pre><code>## [1] 5</code></pre>
<p>Of course, it is possible to select vertices or edges by positional
indices:</p>
<div class="sourceCode" id="cb78"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb78-1"><a href="#cb78-1" 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="cb78-2"><a href="#cb78-2" tabindex="-1"></a>seq</span></code></pre></div>
<pre><code>## + 3/10 vertices, from 81614be:
## [1] 2 3 7</code></pre>
<div class="sourceCode" id="cb80"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb80-1"><a href="#cb80-1" tabindex="-1"></a>seq <span class="ot">&lt;-</span> seq[<span class="dv">1</span>, <span class="dv">3</span>] <span class="co"># filtering an existing vertex set</span></span>
<span id="cb80-2"><a href="#cb80-2" tabindex="-1"></a>seq</span></code></pre></div>
<pre><code>## + 2/10 vertices, from 81614be:
## [1] 2 7</code></pre>
<p>Selecting a vertex that does not exist results in an error:</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>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>
<span id="cb82-2"><a href="#cb82-2" tabindex="-1"></a><span class="do">## Error in simple_vs_index(x, ii, na_ok) : Unknown vertex selected</span></span></code></pre></div>
<p>Attribute names can also be used as-is within the indexing brackets
of <code>V()</code> and <code>E()</code>. This can be combined with R’s
ability to use Boolean vectors for indexing to obtain very concise and
readable expressions to retrieve a subset of the vertex or edge set of a
graph. For instance, the following command gives you the names of the
individuals younger than 30 years in our social network:</p>
<div class="sourceCode" id="cb83"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb83-1"><a href="#cb83-1" 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>Of course, <code>&lt;</code> is not the only boolean operator that
can be used for this. Other possibilities include the following:</p>
<table>
<colgroup>
<col width="29%" />
<col width="70%" />
</colgroup>
<thead>
<tr class="header">
<th>Operator</th>
<th>Meaning</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>==</code></td>
<td>The attribute/property value must be <em>equal to</em></td>
</tr>
<tr class="even">
<td><code>!=</code></td>
<td>The attribute/property value must <em>not be equal to</em></td>
</tr>
<tr class="odd">
<td><code>&lt;</code></td>
<td>The attribute/property value must be <em>less than</em></td>
</tr>
<tr class="even">
<td><code>&lt;=</code></td>
<td>The attribute/property value must be <em>less than or equal
to</em></td>
</tr>
<tr class="odd">
<td><code>&gt;</code></td>
<td>The attribute/property value must be <em>greater than</em></td>
</tr>
<tr class="even">
<td><code>&gt;=</code></td>
<td>The attribute/property value must be <em>greater than or equal
to</em></td>
</tr>
<tr class="odd">
<td><code>%in%</code></td>
<td>The attribute/property value must be <em>included in</em></td>
</tr>
</tbody>
</table>
<p>You can also create a “not in” operator from <code>%in%</code> using
the <code>Negate()</code> function:</p>
<div class="sourceCode" id="cb85"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb85-1"><a href="#cb85-1" 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>If an attribute has the same name as an <code>igraph</code> function,
you should be careful as the syntax can become a little confusing. For
instance, if there is an attribute named <code>degree</code> that
represents the grades of an exam for each person, that should not be
confused with the <code>igraph</code> function that computes the degrees
of vertices in a network sense:</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">V</span>(g)<span class="sc">$</span>degree <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;A&quot;</span>, <span class="st">&quot;B&quot;</span>, <span class="st">&quot;B+&quot;</span>, <span class="st">&quot;A+&quot;</span>, <span class="st">&quot;C&quot;</span>, <span class="st">&quot;A&quot;</span>, <span class="st">&quot;B&quot;</span>)</span>
<span id="cb86-2"><a href="#cb86-2" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</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="cb88"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb88-1"><a href="#cb88-1" 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="selecting-edges" class="section level3">
<h3>Selecting edges</h3>
<p>Edges can be selected based on attributes just like vertices. As
mentioned above, the standard way to get edges is <code>E</code>.
Moreover, there are a few special structural properties for selecting
edges.</p>
<p>Using <code>.from()</code> allows you to filter the edge sequence
based on the source vertices of the edges. For instance, to select all
the edges originating from Carmina (who has vertex index 3):</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="fu">.from</span>(<span class="dv">3</span>)]</span></code></pre></div>
<pre><code>## + 4/9 edges from 64d92f3 (vertex names):
## [1] Alejandra--Carmina Carmina  --Moshe   Carmina  --Nang    Carmina  --Samira</code></pre>
<p>Of course it also works with vertex names:</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">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 64d92f3 (vertex names):
## [1] Alejandra--Carmina Carmina  --Moshe   Carmina  --Nang    Carmina  --Samira</code></pre>
<p>Using <code>.to()</code> filters edge sequences based on the target
vertices. This is different from <code>.from()</code> if the graph is
directed, while it gives the same answer for undirected graphs. Using
<code>.inc()</code> selects only those edges that are incident on a
single vertex or at least one of the vertices, irrespective of the edge
directions.</p>
<p>The <code>%--%</code> operator can be used to select edges between
specific groups of vertices, ignoring edge directions in directed
graphs. For instance, the following expression selects all the edges
between Carmina (vertex index 3), Nang (vertex index 5) and Samira
(vertex index 6):</p>
<div class="sourceCode" id="cb94"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb94-1"><a href="#cb94-1" 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 64d92f3 (vertex names):
## [1] Carmina--Nang   Carmina--Samira Nang   --Samira</code></pre>
<p>To make the <code>%--%</code> operator work with names, you can build
string vectors containing the names and then use these vectors as
operands. For instance, to select all the edges that connect men to
women, we can do the following after re-adding the gender attribute that
we deleted earlier:</p>
<div class="sourceCode" id="cb96"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb96-1"><a href="#cb96-1" 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="cb97"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb97-1"><a href="#cb97-1" 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">$</span>name</span>
<span id="cb97-2"><a href="#cb97-2" tabindex="-1"></a>men</span></code></pre></div>
<pre><code>## [1] &quot;Bruno&quot;   &quot;Moshe&quot;   &quot;Nang&quot;    &quot;Ibrahim&quot;</code></pre>
<div class="sourceCode" id="cb99"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb99-1"><a href="#cb99-1" tabindex="-1"></a>women <span class="ot">&lt;-</span> <span class="fu">V</span>(g)[gender <span class="sc">==</span> <span class="st">&quot;f&quot;</span>]<span class="sc">$</span>name</span>
<span id="cb99-2"><a href="#cb99-2" 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="cb101"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb101-1"><a href="#cb101-1" 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 64d92f3 (vertex names):
## [1] Alejandra--Bruno  Alejandra--Moshe  Carmina  --Moshe  Carmina  --Nang  
## [5] Nang     --Samira</code></pre>
</div>
</div>
<div id="treating-a-graph-as-an-adjacency-matrix" class="section level2">
<h2>Treating a graph as an adjacency matrix</h2>
<p>The adjacency matrix is another way to represent a graph. In an
adjacency matrix, rows and columns are labeled by graph vertices, and
the elements of the matrix indicate the number of edges between vertices
<em>i</em> and <em>j</em>. The adjacency matrix for the example graph
is:</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><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>For example, Carmina (<code>1, 0, 0, 1, 1, 1, 0</code>) is directly
connected to Alejandra (who has vertex index 1), Moshe (index 4), Nang
(index 5) and Samira (index 6), but not to Bruno (index 2) or to Ibrahim
(index 7).</p>
</div>
<div id="layouts-and-plotting" class="section level2">
<h2>Layouts and plotting</h2>
<p>A graph is an abstract mathematical object without a specific
representation in 2D, 3D or any other geometric space. This means that
whenever we want to visualise a graph, we have to find a mapping from
vertices to coordinates in two- or three-dimensional space first,
preferably in a way that is useful and/or pleasing for the eye. A
separate branch of graph theory, namely graph drawing, tries to solve
this problem via several graph layout algorithms. igraph implements
quite a few layout algorithms and is also able to draw them onto the
screen or to any output format that R itself supports.</p>
<div id="layout-algorithms" class="section level3">
<h3>Layout algorithms</h3>
<p>The layout functions in igraph always start with <code>layout</code>.
The following table summarises them:</p>
<table>
<colgroup>
<col width="20%" />
<col width="79%" />
</colgroup>
<thead>
<tr class="header">
<th>Method name</th>
<th>Algorithm description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>layout_randomly</code></td>
<td>Places the vertices completely randomly</td>
</tr>
<tr class="even">
<td><code>layout_in_circle</code></td>
<td>Deterministic layout that places the vertices on a circle</td>
</tr>
<tr class="odd">
<td><code>layout_on_sphere</code></td>
<td>Deterministic layout that places the vertices evenly on the surface
of a sphere</td>
</tr>
<tr class="even">
<td><code>layout_with_drl</code></td>
<td>The Drl (Distributed Recursive Layout) algorithm for large
graphs</td>
</tr>
<tr class="odd">
<td><code>layout_with_fr</code></td>
<td>Fruchterman-Reingold force-directed algorithm</td>
</tr>
<tr class="even">
<td><code>layout_with_kk</code></td>
<td>Kamada-Kawai force-directed algorithm</td>
</tr>
<tr class="odd">
<td><code>layout_with_lgl</code></td>
<td>The LGL (Large Graph Layout) algorithm for large graphs</td>
</tr>
<tr class="even">
<td><code>layout_as_tree</code></td>
<td>Reingold-Tilford tree layout, useful for (almost) tree-like
graphs</td>
</tr>
<tr class="odd">
<td><code>layout_nicely</code></td>
<td>Layout algorithm that automatically picks one of the other
algorithms based on certain properties of the graph</td>
</tr>
</tbody>
</table>
<p>Layout algorithms can be called directly with a graph as its first
argument. They will return a matrix with two columns and as many rows as
the number of vertices in the graph; each row will correspond to the
position of a single vertex, ordered by vertex IDs. Some algorithms have
a 3D variant; in this case they return 3 columns instead of 2.</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>layout <span class="ot">&lt;-</span> <span class="fu">layout_with_kk</span>(g)</span></code></pre></div>
<p>Some layout algorithms take additional arguments; for instance, when
laying out a graph as a tree, it might make sense to specify which
vertex is to be placed at the root of the layout:</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>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="drawing-a-graph-using-a-layout" class="section level3">
<h3>Drawing a graph using a layout</h3>
<p>We can plot our imaginary social network with the Kamada-Kawai layout
algorithm as follows:</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>layout <span class="ot">&lt;-</span> <span class="fu">layout_with_kk</span>(g)</span></code></pre></div>
<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>(g, <span class="at">layout =</span> layout, <span class="at">main =</span> <span class="st">&quot;Social network with the Kamada-Kawai layout algorithm&quot;</span>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>This should open a new window showing a visual representation of the
network. Remember that the exact placement of nodes may be different on
your machine since the layout is not deterministic.</p>
<p>The <code>layout</code> argument also accepts functions; in this
case, the function will be called with the graph as its first argument.
This makes it possible to just pass the name of a layout function
directly, without creating a layout variable:</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">plot</span>(</span>
<span id="cb109-2"><a href="#cb109-2" tabindex="-1"></a>  g,</span>
<span id="cb109-3"><a href="#cb109-3" tabindex="-1"></a>  <span class="at">layout =</span> layout_with_fr,</span>
<span id="cb109-4"><a href="#cb109-4" tabindex="-1"></a>  <span class="at">main =</span> <span class="st">&quot;Social network with the Fruchterman-Reingold layout algorithm&quot;</span></span>
<span id="cb109-5"><a href="#cb109-5" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>To improve the visuals, a trivial addition would be to color the
vertices according to the gender. We should also try to place the labels
slightly outside the vertices to improve readability:</p>
<div class="sourceCode" id="cb110"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb110-1"><a href="#cb110-1" 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="cb110-2"><a href="#cb110-2" tabindex="-1"></a><span class="fu">plot</span>(</span>
<span id="cb110-3"><a href="#cb110-3" tabindex="-1"></a>  g,</span>
<span id="cb110-4"><a href="#cb110-4" tabindex="-1"></a>  <span class="at">layout =</span> layout, <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>,</span>
<span id="cb110-5"><a href="#cb110-5" tabindex="-1"></a>  <span class="at">main =</span> <span class="st">&quot;Social network - with genders as colors&quot;</span></span>
<span id="cb110-6"><a href="#cb110-6" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>You can also treat the <code>gender</code> attribute as a factor and
provide the colors with an argument to <code>plot()</code>, which takes
precedence over the <code>color</code> vertex attribute. Colors will be
assigned automatically to levels of a factor:</p>
<div class="sourceCode" id="cb111"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb111-1"><a href="#cb111-1" tabindex="-1"></a><span class="fu">plot</span>(g, <span class="at">layout =</span> layout, <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>, <span class="at">vertex.color =</span> <span class="fu">as.factor</span>(<span class="fu">V</span>(g)<span class="sc">$</span>gender))</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<p>As seen above with the <code>vertex.color</code> argument, you can
specify visual properties as arguments to <code>plot</code> instead of
using vertex or edge attributes. The following plot shows the formal
ties with thick lines while informal ones with thin lines:</p>
<div class="sourceCode" id="cb112"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb112-1"><a href="#cb112-1" tabindex="-1"></a><span class="fu">plot</span>(g,</span>
<span id="cb112-2"><a href="#cb112-2" tabindex="-1"></a>  <span class="at">layout =</span> layout, <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>, <span class="at">vertex.size =</span> <span class="dv">20</span>,</span>
<span id="cb112-3"><a href="#cb112-3" tabindex="-1"></a>  <span class="at">vertex.color =</span> <span class="fu">ifelse</span>(<span class="fu">V</span>(g)<span class="sc">$</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="cb112-4"><a href="#cb112-4" 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="cb112-5"><a href="#cb112-5" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>This latter approach is preferred if you want to keep the properties
of the visual representation of your graph separate from the graph
itself.</p>
<p>In summary, there are special vertex and edge properties that
correspond to the visual representation of the graph. These attributes
override the default settings of igraph (i.e color, weight, name, shape,
layout, etc.). The following two tables summarise the most frequently
used visual attributes for vertices and edges, respectively:</p>
</div>
<div id="vertex-attributes-controlling-graph-plots" class="section level3">
<h3>Vertex attributes controlling graph plots</h3>
<table>
<colgroup>
<col width="30%" />
<col width="30%" />
<col width="39%" />
</colgroup>
<thead>
<tr class="header">
<th>Attribute name</th>
<th>Keyword argument</th>
<th>Purpose</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>color</code></td>
<td><code>vertex.color</code></td>
<td>Color of the vertex</td>
</tr>
<tr class="even">
<td><code>label</code></td>
<td><code>vertex.label</code></td>
<td>Label of the vertex. They will be converted to character. Specify NA
to omit vertex labels. The default vertex labels are the vertex
ids.</td>
</tr>
<tr class="odd">
<td><code>label.cex</code></td>
<td><code>vertex.label.cex</code></td>
<td>Font size of the vertex label, interpreted as a multiplicative
factor, similarly to R’s <code>text</code> function</td>
</tr>
<tr class="even">
<td><code>label.color</code></td>
<td><code>vertex.label.color</code></td>
<td>Color of the vertex label</td>
</tr>
<tr class="odd">
<td><code>label.degree</code></td>
<td><code>vertex.label.degree</code></td>
<td>It defines the position of the vertex labels, relative to the center
of the vertices. It is interpreted as an angle in radian, zero means ‘to
the right’, and ‘pi’ means to the left, up is -pi/2 and down is pi/2.
The default value is -pi/4</td>
</tr>
<tr class="even">
<td><code>label.dist</code></td>
<td><code>vertex.label.dist</code></td>
<td>Distance of the vertex label from the vertex itself, relative to the
vertex size</td>
</tr>
<tr class="odd">
<td><code>label.family</code></td>
<td><code>vertex.label.family</code></td>
<td>Font family of the vertex, similarly to R’s <code>text</code>
function</td>
</tr>
<tr class="even">
<td><code>label.font</code></td>
<td><code>vertex.label.font</code></td>
<td>Font within the font family of the vertex, similarly to R’s
<code>text</code> function</td>
</tr>
<tr class="odd">
<td><code>shape</code></td>
<td><code>vertex.shape</code></td>
<td>The shape of the vertex, currently “circle”, “square”, “csquare”,
“rectangle”, “crectangle”, “vrectangle”, “pie” (see vertex.shape.pie),
‘sphere’, and “none” are supported, and only by the plot.igraph
command.</td>
</tr>
<tr class="even">
<td><code>size</code></td>
<td><code>vertex.size</code></td>
<td>The size of the vertex, a numeric scalar or vector, in the latter
case each vertex sizes may differ</td>
</tr>
</tbody>
</table>
</div>
<div id="edge-attributes-controlling-graph-plots" class="section level3">
<h3>Edge attributes controlling graph plots</h3>
<table>
<colgroup>
<col width="34%" />
<col width="40%" />
<col width="25%" />
</colgroup>
<thead>
<tr class="header">
<th>Attribute name</th>
<th>Keyword argument</th>
<th>Purpose</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>color</code></td>
<td><code>edge.color</code></td>
<td>Color of the edge</td>
</tr>
<tr class="even">
<td><code>curved</code></td>
<td><code>edge.curved</code></td>
<td>A numeric value specifies the curvature of the edge; zero curvature
means straight edges, negative values means the edge bends clockwise,
positive values the opposite. TRUE means curvature 0.5, FALSE means
curvature zero</td>
</tr>
<tr class="odd">
<td><code>arrow.size</code></td>
<td><code>edge.arrow.size</code></td>
<td>Currently this is a constant, so it is the same for every edge. If a
vector is submitted then only the first element is used, that is to say
if this is taken from an edge attribute then only the attribute of the
first edge is used for all arrows.</td>
</tr>
<tr class="even">
<td><code>arrow.width</code></td>
<td><code>edge.arrow.width</code></td>
<td>The width of the arrows. Currently this is a constant, so it is the
same for every edge</td>
</tr>
<tr class="odd">
<td><code>width</code></td>
<td><code>edge.width</code></td>
<td>Width of the edge in pixels</td>
</tr>
<tr class="even">
<td><code>label</code></td>
<td><code>edge.label</code></td>
<td>If specified, it adds a label to the edge.</td>
</tr>
<tr class="odd">
<td><code>label.cex</code></td>
<td><code>edge.label.cex</code></td>
<td>Font size of the edge label, interpreted as a multiplicative factor,
similarly to R’s <code>text</code> function</td>
</tr>
<tr class="even">
<td><code>label.color</code></td>
<td><code>edge.label.color</code></td>
<td>Color of the edge label</td>
</tr>
<tr class="odd">
<td><code>label.family</code></td>
<td><code>edge.label.family</code></td>
<td>Font family of the edge, similarly to R’s <code>text</code>
function</td>
</tr>
<tr class="even">
<td><code>label.font</code></td>
<td><code>edge.label.font</code></td>
<td>Font within the font family of the edge, similarly to R’s
<code>text</code> function</td>
</tr>
</tbody>
</table>
</div>
<div id="generic-arguments-of-plot" class="section level3">
<h3>Generic arguments of <code>plot()</code></h3>
<p>These settings can be specified as arguments to the <code>plot</code>
function to control the overall appearance of the plot.</p>
<table>
<colgroup>
<col width="44%" />
<col width="55%" />
</colgroup>
<thead>
<tr class="header">
<th>Keyword argument</th>
<th>Purpose</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>layout</code></td>
<td>The layout to be used. It can be an instance of <code>Layout</code>,
a list of tuples containing X-Y coordinates, or the name of a layout
algorithm. The default is <code>auto</code>, which selects a layout
algorithm automatically based on the size and connectedness of the
graph.</td>
</tr>
<tr class="even">
<td><code>margin</code></td>
<td>The amount of empty space below, over, at the left and right of the
plot, it is a numeric vector of length four.</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="igraph-and-the-outside-world" class="section level2">
<h2>igraph and the outside world</h2>
<p>No graph module would be complete without some kind of import/export
functionality that enables the package to communicate with external
programs and toolkits. <code>igraph</code> is no exception: it provides
functions to read the most common graph formats and to save graphs into
files obeying these format specifications. The main functions for
reading and writing from/to file are <code>read_graph()</code> and
<code>write_graph()</code>, respectively. The following table summarises
the formats igraph can read or write:</p>
<table>
<colgroup>
<col width="25%" />
<col width="25%" />
<col width="25%" />
<col width="25%" />
</colgroup>
<thead>
<tr class="header">
<th>Format</th>
<th>Short name</th>
<th>Read function</th>
<th>Write function</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Adjacency list (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>Adjacency matrix</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>NOTE:</strong> Each file format has its own limitations. For
instance, not all of them can store attributes. Your best bet is
probably GraphML or GML if you want to save igraph graphs in a format
that can be read from an external package and you want to preserve
numeric and string attributes. Edge list and NCOL is also fine if you
don’t have attributes (NCOL supports vertex names and edge weights,
though).</p>
<hr />
</div>
<div id="where-to-go-next" class="section level2">
<h2>Where to go next</h2>
<p>This tutorial is a brief introduction to <code>igraph</code> in R. We
sincerely hope you enjoyed reading it and that it will be useful for
your own network analyses.</p>
<p>For a detailed description of specific functions, see <a href="https://r.igraph.org/reference/" class="uri">https://r.igraph.org/reference/</a>. For questions on how to
use <code>igraph</code>, please visit our <a href="https://igraph.discourse.group">Forum</a>. To report a bug, open a
<a href="https://github.com/igraph/rigraph/issues">Github issue</a>.
Please do not ask usage questions on Github directly as it’s meant for
developers rather than users.</p>
</div>
<div id="session-info" class="section level2">
<h2>Session info</h2>
<p>For the sake of reproducibility, the session information for the code
above is the following:</p>
<div class="sourceCode" id="cb113"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb113-1"><a href="#cb113-1" 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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