File: igraph.html

package info (click to toggle)
r-cran-igraph 2.1.4-1
links: PTS, VCS
area: main
in suites: forky, trixie
size: 27,044 kB
sloc: ansic: 204,981; cpp: 21,711; fortran: 4,090; yacc: 1,229; lex: 519; sh: 52; makefile: 8
file content (1460 lines) | stat: -rw-r--r-- 585,690 bytes
<!DOCTYPE html>

<html>

<head>

<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />

<meta name="viewport" content="width=device-width, initial-scale=1" />



<title>igraph (R interface)</title>

<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
  var i, h, a;
  for (i = 0; i < hs.length; i++) {
    h = hs[i];
    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
    a = h.attributes;
    while (a.length > 0) h.removeAttribute(a[0].name);
  }
});
</script>

<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>



<style type="text/css">
code {
white-space: pre;
}
.sourceCode {
overflow: visible;
}
</style>
<style type="text/css" data-origin="pandoc">
html { -webkit-text-size-adjust: 100%; }
pre > code.sourceCode { white-space: pre; position: relative; }
pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
pre > code.sourceCode > span:empty { height: 1.2em; }
.sourceCode { overflow: visible; }
code.sourceCode > span { color: inherit; text-decoration: inherit; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
pre > code.sourceCode { white-space: pre-wrap; }
pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
}
pre.numberSource code
{ counter-reset: source-line 0; }
pre.numberSource code > span
{ position: relative; left: -4em; counter-increment: source-line; }
pre.numberSource code > span > a:first-child::before
{ content: counter(source-line);
position: relative; left: -1em; text-align: right; vertical-align: baseline;
border: none; display: inline-block;
-webkit-touch-callout: none; -webkit-user-select: none;
-khtml-user-select: none; -moz-user-select: none;
-ms-user-select: none; user-select: none;
padding: 0 4px; width: 4em;
color: #aaaaaa;
}
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
div.sourceCode
{ }
@media screen {
pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
}
code span.al { color: #ff0000; font-weight: bold; } 
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } 
code span.at { color: #7d9029; } 
code span.bn { color: #40a070; } 
code span.bu { color: #008000; } 
code span.cf { color: #007020; font-weight: bold; } 
code span.ch { color: #4070a0; } 
code span.cn { color: #880000; } 
code span.co { color: #60a0b0; font-style: italic; } 
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } 
code span.do { color: #ba2121; font-style: italic; } 
code span.dt { color: #902000; } 
code span.dv { color: #40a070; } 
code span.er { color: #ff0000; font-weight: bold; } 
code span.ex { } 
code span.fl { color: #40a070; } 
code span.fu { color: #06287e; } 
code span.im { color: #008000; font-weight: bold; } 
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } 
code span.kw { color: #007020; font-weight: bold; } 
code span.op { color: #666666; } 
code span.ot { color: #007020; } 
code span.pp { color: #bc7a00; } 
code span.sc { color: #4070a0; } 
code span.ss { color: #bb6688; } 
code span.st { color: #4070a0; } 
code span.va { color: #19177c; } 
code span.vs { color: #4070a0; } 
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } 
</style>
<script>
// apply pandoc div.sourceCode style to pre.sourceCode instead
(function() {
  var sheets = document.styleSheets;
  for (var i = 0; i < sheets.length; i++) {
    if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
    try { var rules = sheets[i].cssRules; } catch (e) { continue; }
    var j = 0;
    while (j < rules.length) {
      var rule = rules[j];
      // check if there is a div.sourceCode rule
      if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") {
        j++;
        continue;
      }
      var style = rule.style.cssText;
      // check if color or background-color is set
      if (rule.style.color === '' && rule.style.backgroundColor === '') {
        j++;
        continue;
      }
      // replace div.sourceCode by a pre.sourceCode rule
      sheets[i].deleteRule(j);
      sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
    }
  }
})();
</script>




<style type="text/css">body {
background-color: #fff;
margin: 1em auto;
max-width: 700px;
overflow: visible;
padding-left: 2em;
padding-right: 2em;
font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
font-size: 14px;
line-height: 1.35;
}
#TOC {
clear: both;
margin: 0 0 10px 10px;
padding: 4px;
width: 400px;
border: 1px solid #CCCCCC;
border-radius: 5px;
background-color: #f6f6f6;
font-size: 13px;
line-height: 1.3;
}
#TOC .toctitle {
font-weight: bold;
font-size: 15px;
margin-left: 5px;
}
#TOC ul {
padding-left: 40px;
margin-left: -1.5em;
margin-top: 5px;
margin-bottom: 5px;
}
#TOC ul ul {
margin-left: -2em;
}
#TOC li {
line-height: 16px;
}
table {
margin: 1em auto;
border-width: 1px;
border-color: #DDDDDD;
border-style: outset;
border-collapse: collapse;
}
table th {
border-width: 2px;
padding: 5px;
border-style: inset;
}
table td {
border-width: 1px;
border-style: inset;
line-height: 18px;
padding: 5px 5px;
}
table, table th, table td {
border-left-style: none;
border-right-style: none;
}
table thead, table tr.even {
background-color: #f7f7f7;
}
p {
margin: 0.5em 0;
}
blockquote {
background-color: #f6f6f6;
padding: 0.25em 0.75em;
}
hr {
border-style: solid;
border: none;
border-top: 1px solid #777;
margin: 28px 0;
}
dl {
margin-left: 0;
}
dl dd {
margin-bottom: 13px;
margin-left: 13px;
}
dl dt {
font-weight: bold;
}
ul {
margin-top: 0;
}
ul li {
list-style: circle outside;
}
ul ul {
margin-bottom: 0;
}
pre, code {
background-color: #f7f7f7;
border-radius: 3px;
color: #333;
white-space: pre-wrap; 
}
pre {
border-radius: 3px;
margin: 5px 0px 10px 0px;
padding: 10px;
}
pre:not([class]) {
background-color: #f7f7f7;
}
code {
font-family: Consolas, Monaco, 'Courier New', monospace;
font-size: 85%;
}
p > code, li > code {
padding: 2px 0px;
}
div.figure {
text-align: center;
}
img {
background-color: #FFFFFF;
padding: 2px;
border: 1px solid #DDDDDD;
border-radius: 3px;
border: 1px solid #CCCCCC;
margin: 0 5px;
}
h1 {
margin-top: 0;
font-size: 35px;
line-height: 40px;
}
h2 {
border-bottom: 4px solid #f7f7f7;
padding-top: 10px;
padding-bottom: 2px;
font-size: 145%;
}
h3 {
border-bottom: 2px solid #f7f7f7;
padding-top: 10px;
font-size: 120%;
}
h4 {
border-bottom: 1px solid #f7f7f7;
margin-left: 8px;
font-size: 105%;
}
h5, h6 {
border-bottom: 1px solid #ccc;
font-size: 105%;
}
a {
color: #0033dd;
text-decoration: none;
}
a:hover {
color: #6666ff; }
a:visited {
color: #800080; }
a:visited:hover {
color: #BB00BB; }
a[href^="http:"] {
text-decoration: underline; }
a[href^="https:"] {
text-decoration: underline; }

code > span.kw { color: #555; font-weight: bold; } 
code > span.dt { color: #902000; } 
code > span.dv { color: #40a070; } 
code > span.bn { color: #d14; } 
code > span.fl { color: #d14; } 
code > span.ch { color: #d14; } 
code > span.st { color: #d14; } 
code > span.co { color: #888888; font-style: italic; } 
code > span.ot { color: #007020; } 
code > span.al { color: #ff0000; font-weight: bold; } 
code > span.fu { color: #900; font-weight: bold; } 
code > span.er { color: #a61717; background-color: #e3d2d2; } 
</style>




</head>

<body>




<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 a256a25 UN-- 10 2 -- 
## + attr: name (v/c)
## + edges from a256a25 (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 a256a25 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" src="data:image/png;base64,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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(g, edges = c(38, 37)): 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 650f453 U--- 40 86 -- Zachary
## + attr: name (g/c)
## + edges from 650f453:
##  [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" src="data:image/png;base64,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" /><!-- --></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 e16d000 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 9712d42 U--- 100 522 -- 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 036c866 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 036c866:
## [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 the R’s
standard function <code>dput()</code> function and retrieve it later
with <code>dget()</code>. You can also just save the R workspace and
restore it later.</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 every 8 hours.
## 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 2b1e940:
## [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 2b1e940:
## [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 036c866 (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 036c866 (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 036c866 (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 036c866 (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" src="data:image/png;base64,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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" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAJACAYAAABlmtk2AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAJAoAMABAAAAAEAAAJAAAAAAC2FqgIAAEAASURBVHgB7J0HeBTH2cf/6pVeRZVAojeDAWNMxw0bcAmOey9J3GP7S2I7iUuq417jFpfEdmxwIQZsAzaY3rtNR3QQEgjU+33zjrzHnnR3upPuTnd7/3ke6XZnp72/2Z19d+admQibcqAjARIgARIgARIggTAiEBlGslJUEiABEiABEiABEtAEqADxRiABEiABEiABEgg7AlSAwq7KKTAJkAAJkAAJkAAVIN4DJEACJEACJEACYUeAClDYVTkFJgESIAESIAESoALEe4AESIAESIAESCDsCFABCrsqp8AkQAIkQAIkQAJUgHgPkAAJkAAJkAAJhB0BKkBhV+UUmARIgARIgARIgAoQ7wESIAESIAESIIGwI0AFKOyqnAKTAAmQAAmQAAlQAeI9QAIkQAIkQAIkEHYEqACFXZVTYBIgARIgARIgASpAvAdIgARIgARIgATCjgAVoLCrcgpMAiRAAiRAAiRABYj3AAmQAAmQAAmQQNgRoAIUdlVOgUmABEiABEiABKgA8R4gARIgARIgARIIOwJUgMKuyikwCZAACZAACZAAFSDeAyRAAiRAAiRAAmFHgApQ2FU5BSYBEiABEiABEqACxHuABEiABEiABEgg7AhQAQq7KqfAJEACJEACJEACVIB4D5AACZAACZAACYQdASpAYVflFJgESIAESIAESIAKEO8BEiABEiABEiCBsCNABSjsqpwCkwAJkAAJkAAJUAHiPUACJEACJEACJBB2BKgAhV2VU2ASIAESIAESIAEqQLwHSIAESIAESIAEwo4AFaCwq3IKTAIkQAIkQAIkQAWI9wAJkAAJkAAJkEDYEYj2tcTbt2/HCy+8gM2bN+PHH39EkyZNkJqaimnTpuG2225DbGysr7OslV5WVhauuuoq7X/dddfhpptuqhXGnUdD47tL29trVVVV2LJlCwYMGOBtVL+H/+1vf4tVq1ahefPm+Oyzz/yenzmDp556Cl9//bX2mj9/PiIjT+vyzpj97W9/w9y5c3X47777zpyU18eZmZlo2bIlmjVrpuMeOXIE11xzjT6+4YYbIH/+dPJ8zZw5020WXbt2xTvvvOM2jD8v1peJs7rzZzlDIe3nnnsOX375Za2iRkdHIzk5GR07dsTkyZNx3nnn1QrjiUcwtXeuyuvuea8ZxyzP9ddfjxtvvLFmEPu5N+naIwXxgT+en2Bj5K48zuR32/bbfOjmzJljUy8Fm7o/nP4NGjTIVlRU5MMcnSe1d+9ee/6PPvqo80BufBsa303SXl1aunSpbciQITb1cvUqXqACX3DBBZpz69atA5WlPR/VqNnruKKiwu7viplqCO3h7YG9PCgoKLA98sgjtvj4eNvOnTvtsffs2WNP+49//KPd318Hv/rVr+z5uXrWevfu7a/sPUq3Pkxc1Z1HGVo40C9+8Ys66zsiIsL2hz/8oV4UgqW9c1d4V8+7szhmeX7/+987C2L38yZde6QgPfDX8xNsjFyVx5X87tp+n/UAqXsCt956K06dOoWmTZvi0ksvxTnnnAP1osD06dMhX80bNmyAekFANDh/usTEREyZMkVnoV4E/szKr2mPHDlSp9+rVy+/5hOKiZ9xxhk4ceKELrpq/O0i+JPZ008/jT//+c/2vILh4IorrkCbNm1qFaV9+/a1/ILdw591F+yye1q+n//851AfHJD2Vr52laKpezbl/IknntDtnvpo8jQ5Hc4q7aVXQlswcLg8P75s+32mAK1fvx6HDx/Wt9VDDz0E1fNiv8Vk+Gvo0KH6XIYtXClA2dnZeiijVatW9riuDiorK3HgwAF07twZUVFRDsHkheBuiEDiSjdpXl4eWrRogXbt2jnEb+iJpFtYWIiUlBS3SUk4URhFhsZy3jCXMh48eBCdOnVqrOLa873nnnsgfw1xJ0+eRFlZGdq2bduQZJzGlRfSvn37NCsZqnDlGnoP/O53v4PqWXWVvFN/uedkyFCGpxvi3D2DztL1lImzuIaft7zqul/lutS/eWheyiltiwwt1WxbjHLUpw2RdPfv36/bBXN+Rpqe/D788MO1hsNF8ZEPS3HffPMNaipABneRU5Sdmq6u9tII72n5VY+sbidkGNb8cWKk4+zX23aoZhoSX+pKhqZ96fLz8yHDuTExMZCPioSEBJ8l31CZfVGQ+tzHNfMVOYSPmEJ46ryV3fwc+7Ltly8Jnzhl82PvppVhG/MQgWSg7C5sCxcutK1du9YhPxkSu/vuu22ql8MeXzU8tmeeecamKschrJyoniSbDL3ExcXp8EqBsV1++eU2ZYNiD6saGVtSUpL+U42D3V8O5FxVlD0vVWE2ZaNk+/bbb+3hzF2odQ2h3XLLLTqfs88+27Z161bbsGHDbOrlotNXDYBN2Z3Y0zUOli1bZuvfv79Nuq0lfynPvffeayspKdFB1qxZo9OUa/KnXqD6/KWXXrKde+65+njs2LFGcvq3e/fu2r9Hjx4O/iNGjND+EyZMsPt7ylzKaXCcNWuWTdKW8iiFzXb8+HFdD3JuHgJ7/fXX7XEmTpxoKy4utudrPmioHHfccYc9HxkCc8dM8jV3gyo7Ndvw4cPt/DMyMmxLliwxF6/W8WWXXWZTD7mWX2RWDaFNNYg6nHm4R4YhnnzySfs9JsNlMoRh1K2RcF33gBHO2a95CEx9eDgL4uB38cUXa1aqV9amPj5s6kWhZVG9WTZlK6evqZegQ5zvv//ezvff//63wzVPnkFvmNRVd5K5J7zqul+lzuV+Fn7//e9/bdLOSF1KHd1+++26vXnllVf0/Sz+qifbdt999znILieetCG7d++281P2O3p4Sn3Y6fyU8mOTbnwZUvXEmYfANm7cWCuKPJtSXvm788477dflnpPyK0VXX5N2aeDAgZqlPZA6cNZe1qf80q5IWYWnlEXa5r/85S+6LTfaEXO+nrZDEsfVsIe8V9LT03V+0p5K+yttrsGjvkNg0parHhV7OpKe1Juy77O3aao3TtexssOyqY9/s2i2xx9/3F7/auTDfs1TmZXdpz3+888/b48vB8rWS18zhrk9eX4cEvjpxJP7WIK6Yi9thPFOkHtLni957uU+l/qW9sXsPJW9rufYl22/zxQgUVbMSozcjKqryqa+ULXiU15ebmahj+XlKC9J42at+SsNt9mJYiKNkhHOeNDkXPL79NNPdXBXCow0REZc9SVkU18L9nN5uW3bts1tfHNZjONrr71WpyEvEHngJR1zupKP0naN4FoRlBeQUQ6zzdSYMWN0OGVYbL9uhJPfZ5991vaPf/xDX5M0RAkRJw+rOZw0XuLkoTT85cUnzhvmohQY8VUvmf34zDPP1GnVtAGShtiQTRlt29QQlQ7n7F9D5aj5ULpjJvmbFSBhLo2Z8UISGeUloXpGnBVV+02aNMkuv8FE7kVx5pe9UZ/Cy2Ah4dUXug4r/6TRNl8z4kg44x6wB3ZyYFaAZsyYYfvhhx8c/oz72IhqPGNyjxpKt+Ql9SWKnRxLg2V2Ukbxlz9lTG2/5Okz6A2TuurOU1513a9yT4o8orjKrzynxoeUnPfr10/71/xAkg8Pw3nahuzatUunJekaLwnhb3wcif///d//Gcm6/XWnAKmebP1RJOnJ37vvvmtPSz56DH+5v4285YPK/GHmrL2sT/mlvTbyk7xEMZBzQ9GUY8N50w5JnJrPu/jJi9KQSdI23g3mOq2PAiTvMlGkDFk6dOjgkI+0JeI+//xzexjzPSLXRDmR+H379pVT7byRedOmTfa0pa00u9GjR+tr8oEtrq7nRweq8c/T+1iiOWO/cuVKhzbMYC/PltG2PfbYY/ZcvZG9rue4Znnqkt9t228voQ8OpBdIbhbjxjH/iv/bb7/tkIu5IZdCyot88eLFtvHjx9vTePPNN+1xfvazn2l/ASw9DaJRyo1ifOHITafGxW3OHmhJRJQVafREg1ZDHzqsmlFjz8son6v49oKYDgwFSGQVhUANq+gvSflSMOSXl5Q46akwGlm5UURzF8VQNHwj7P/+9z/dWyA9aIafsmfSPWq5ubk2c8P00Ucf6XRffvlle1iJ89Zbb2l/+TXSkHjivGFuvhElHSmnGlq0ffXVVzotswJk/gqRL7KjR4/qMK7+NVSOmg+BfO26YiZlMD8EUm41RKnr/6677rIzkgbNlTt06JDuqTR4zps3z2YomuaXvbxc1DCETkauGw2DfHmL8/Qe0IFd/DPXoVEe86/c42ZnKEASRnoq5eUnCrHce94qQJ4+g94wcVd33vCq6341FCDhII2zvOjUMKWDEqSG73Udyf0sdSlhpYfZcJ62Ieb7W9KZPXu2TkLaSOk9lHSlLfDEmRUguZ/kQ0uUZvMHoKQnvb3CS9zHH39sv6+lF0h6m+R+NBhIL73hnLV33pbfrDBLz4ko4Wq40iY96FI248/I03wPe9L213zeJZ1Ro0bZ03311VdtpaWlWinq06eP3b8+CpD0bMi7RD5mRS5x8nHUpUsXna70gIuTd4gotSKbsnfVfvJPPkgMedUMJLu/NzJ7owC5e37smdc48PQ+lmjO2BtKmMgp72JhIQppz5497bKbP/q8kb2u57hmeeqS323bX4NLg0/lppfGVdn82EEYN4P8/vWvf7XnYdxQ8iVuPLhyUR5UI85ZZ52lw0tjZTzwohCYnTTo8kKSryFxzh5oc3ipLHHyUlOGrfa8ZNhNXF3xdaCf/pkVIPOXt5THkOG1117Toc09NTLsZzh5EckwkoRXRq2Gtz1+zVlgMnwmYaVixV1yySX6XL7ixd8IL0Mecm68fCWsN8zNN+JFF10k0R2coQBJL5fRQyR1JPw8cQ2Ro+ZDYOQn8poZGP7mh8A8DCvDskYcQ3E04tT8lZemEdY8xGt+2avpyA7RZKhS4ii7Ke3v7T3gkNhPJ+bGxCiP+dedArR69WqHJL1RgLx5Br1hYhTIkMG4f8XfG1513a/Gy18+mEQWwxlf69I7Jo2p4WRoVMpkVhaMa3W1IWYF4sorrzSi6V9jaEWeGcPJsKJ8lZv/5IUizqwAGYzMv4MHD9bDDaIAGE6GZySMfCweO3bM8LaZP/jUMiXa31l75235zYqO8WEmiQsnoydIymM4b9ohiVPzeRdZjd6fmsP+Mrxp8KmPAmSU0ahjyUvuLeEs6cr9Y7j7779f+8m9o+xUtLfRTkj5lC2ZEdSrttcbBcjIwJDZ/PwY11z9GjK6ehdKPHfsReExO3m/G+UwK0De1Hddz3HN8hj5G/nWlN9d2+/aOlOlVh+nbg6IEbT8iaGx0qDxn//8B2qKvE5OjY1C3TTaYFoMAsWpl6uDsWG3bt2gXo56LSFZA0cJqI0SVeOkw6sbXv8a/5Q9iXFY569qYLSBtFKadJrmCJJPQ5ysd2Q49aVmHEI9QPpYvTTtfmq4TrMxPFQXoT4U48u6nMywk3WWxKBcKY5QL3EdRdkRQY25Y8GCBdq4V9bHEadecvpXNbLaCFNOPGGuI/30zzBiN/sZx6onDvInTupIDOJV96xx2eVvfeVwmaCHF9LS0uwhzUahYrjeUGdOW9Iy0jfS9tU9YJRT1l9SL3DjVP+ql57DuXGieiJcGkzXvPdlhlFNJ/dmfZ7BupjUzMd8Xl9e7u5X1RvtsG6UYdgqRsJq+MSevfqg0MfG82tc8LYNMbcLkobRNpjTVT1EUL2RRhb6d8yYMfZn27igvrZ1fGk/VM+y9pZJFMr2x8GQ2+CmXsIYN26cEd3+nIqH1GfNe8ce0HTgSfnVR6s9hupxtB+rXhSdvzAznLftkFIujKj2X5lgYNyj559/vt1fDtSwk8N5fU6UQmB/b6nhHnsbLmmZnxVZY07WaRI/qQ9pg416USMZ9gkj3spsLrM5P/E35DaHqc+xt/exkYdSmO1lMNe1XJe1qGRihtk1RHZ3z7E5D0+PzW2RtM2nV4/zNAUX4ZSdAJRxkp7+roZqdCj1haMXJJSHWxkZaj9pQOWGMhod8ZSGuaYzbnrj11zp5oajZjx359JIyPR4NdSlrdblZlVGj/Yo0ljU10k5zY2ns5eQoeRIHgJfFjEz/lR3OJQRWZ0zxySuKA7i1Jcd1BAhZDaTNNbKpkCXQWbjqd4MyAwGcYYC5C1zHfmnf3VZ+IscSsvXoR944AGHBsOcjvm4vnKY06jPsRpCsEcz13nNhsYeyIsDc9oSzUjfSNtX94BRJHmgZZkE85/quTAuO/xKHTl71iSQKNJm5+wZq+8zWBcTc741j+vLy939qnopHbIx2hjVc+bg7+ykPm1IzfyctQ3O8nLmp3rEoXqU9NIiN/y04KbMeJUXkaGcSjyDm+RltDHyK0qetDPyp3rBnGVRy8+T8hsMJXLNe0zZKjqk2ZB2yEjInJ/xkWFck/awIU4+umUmnSz0umLFCihbKq3kGB/axjMteciHuoQV98knn0AWApb44lTPg/6Vfw2R2ZNn056Rhwf1uY+NpM3s66pridMQ2d09x0Z5vPk1t0VSj7U1D29SM4VVXdV44403tI/qaob09JidTL01nEwBVTYwkC8L0SZFE5XG1bixRLtXXYA6uGjzAlx6FETBkIZZjbEaSelfNeQGNayhNf9f//rXDteME9Ho1TixPpX1ikRxECdrExnOyN849/WvWftUBoP6oTLykN4yURidOePlaVyTac8GO9Xdqr3VeLhemVjWgpBeN4O/9JaJciXOW+Y60k//pM5cOXkIpA6ljtVwnF6bRL6KpAFx5+orh7s0jWs1mRn+vvqtb/r1vQd8UW5ndWg0TqorXPcaGmFk6m9N19BnsGZ6rs7NbOvLy5DDVR7O/M0Nu7Pr9W1D6kpX8pJeDHmBmp2yLzGf1jpWtn9QNpP6eVM2S3rpEVmrSpxwkzZZXp7K2F2vHST+soyA9MoY9S5+dTlPyi+99oZTQxiQ9k1cTk6OViKMa/LbkHbISEfaP2l3RD5D4TCuyVIADXGy0rq0x/K+kXeNmmGrkzN62mu+J6QXSN4/y5cvt7fp8kFqfHhKZG9lNtePst9yEMfZs2kOYH5+zP7GcX3vYyO+fOiKYi0KtNS12cm9VtN5K7s5fn2e47rkN6df/y4PcyrqWF58xoOiZivp3gg1hg1lCwNRSoyHWx5qZWGvYytDKv0rawKIRiqKjwyfKBsHe+pqmrk+lpvO6OqUF7wM9YimLw++LE4n6avpuvprxx7ZdLBjxw77mbI5sh9LT4nhat5ohr+vfuWFb/SSqHFq3UBJ2iKP3CSi7RpKmvhLQyVOenTk685Y+E/8zL0ncm50RRq/0jskzvwQyrk3zCW84dx9tUq5x44di6lTp+pfiSN1UteDKuHqK4fEdebcMXMW3hs/I22JI+tSKENvb6LrsN7eA15n4CaCszpUY/j2GMbaWXLvyEdFTdfQZ7BmejXPDb7m+72+vJzJWjM/b8/92YaoiQN6uyBZM834k2fKnZMenXfffdfe7qpJClA2XjqKPIviREFQs4j0h6N8ZIq/xJPF5EQZ8pWTrYeM9l+2PFKzovDFF19oRcjcc2jkV992yIgvyo8MMYkTUwDj/SLD/2Jy0RBn1LPcQ0aPgQzxSdrijOFsIw+RXZQlefEanQDS7hpDqEY4b2QWBc94+Yv5iHygSPovvviibnuMNM2/zp4f83Xj2JBPzuvzLhRZ1cQAnZwofnIsbH7zm9/o0RUjH/OvN7Kb43nzHHsqvzl9geoz9+CDD9oNoFQmtY5Vhdref/99e34yq0nWDjDCKmHtx+InU4/NTgzzVHenPYw5vHr4bOoh0MH3OtkKQ6ZkG9ONJayqEJv6otVpybnkJ2vxiHMWX19w8s8wgpY0zE5mkRhymddx+PDDD+3TBOW6GGCqF4sOK8cyO8lwNZcVMK9JtGjRInv6ko6xHowYuRr5yq9METQ7b5ibjdHE0r+mM4ygzesAiYGxwfNGtdZJXa6+ckjahpyqkbdn44qZ2RDOHlgduKoncxjj2GxcKTJKvYkRodng12z4J/GMqcFKSTSSsXlzD9gjmQ7MRtBGvZsu1zpUSrFmpRbmrHVNKd/2+hKeMoNPnitZV8rgq4a37fE8fQa9ZSIZuKo7T3nVdb8aRtDmSQGSrxg5i6wye8jslPKl/Y3ZWt60IWYjYlkTxuyc3RPm6zWPzUbQztYBknbLqCtZekSeB9VTrpchMfxltpIxUUH8zHXqrL2rT/nVh669HEa+SgmwTwkXP8N50w5JHGfPu9z7ShGy52m07+pjUi9zIfnVxwha1vIyyq+GDG2ynIB6uTo8J1J+s5PJK0Yc+ZVJMDWdtzIrGzB7mtJ+yPMr7Y7q3dP+8v4yO1fPjzmMHHtzH0t4Z+xlLTUpj1lmOTZPflKjExJdO29kr+s5dlYeycSV/O7afp/1ACnh8fe//11vwCjaq9lJd55aOwZqsUHI5qSGk54D6SGSLkRZXdgYkxatW3p41JRwI6j+la5I0Thliw11I9jDS9f8Bx98oL+cHCKYTiRN+UoQA0jFCurFq+PLV6/xJWEYapui+fxQvhbki0WGpkQG6aaWssm4vhjPmcez1UJVDl3VhqGxFEqGuowuclkaXzXquqxqpoJ9NVQxjhTuZuctc3NcT44lf6OO33vvPfsXqau49ZXDVXrumLmK46m/bDhpfMnIPSRfeDXtGzxJy5t7wJP0GhJGDGTlq1K9PHQyMjFB5JTn0plryDPoLD2zn6u6CxZewdKGmJnJsZp5A8PuS3rQpSdIeg9keELshGRiivSyy5+0B9K7p14iNZNp8LmaRavzFsNVaWfEZkZ6t402yOjRkIx80Q5J76D0PBjvGxmCl7xkKMrcjnormKy2ffXVV+to0hsqQ1+y5Yz0aBmu5rtC3mGGk3eZ8U4x/OTXW5llCymx+ZL3hIx2SG+IWt5AP5/mdI1jV8+Pcd349cV9LO8vub/Ushh69ELOxeTB3Pvm6/o2yu/q11P5HeKL5uQPJxrfunXr9Lo15imn7vJS1uJ6XQ53YYxrsq6FrLCpxjP1ei6Gf12/MuVctFf56mlsJ0sGyFRUd3yMtY5kHQp/OG+Y+yN/f6Tpb2aydoyaZePVfedKTk/uAVdxfekv96BMORd2nrr6PoPu0q+r7oKBVzC1Ie5YGtfUEJS+X80LshrXfPUrz4T05sqvuUdW0jd6vKQ3xZVraDsk8etae8xV3q78ZaFZ6XHz5JkwL4ro6QKXnsqs7Kh0L7Orcpr963p+zGEbch8rxVOvwVfzvWTuUZe1mVw5T2V3Fd+VvzfySxp63MZBI+IJCZAACZAACXhBQHp6ZLaUOLFnEkNkmXEmvQRi0yT2lWIYbZ4O70XyQRlUZtqpD309KqHMPyC2NWInpz5qYbatC8rCN7BQ0hMuBvjixMZM7HbFdk8m3xi9QGKfa8yQa2B2fotOBchvaJkwCZAACYQHAZmkIVPrjdm7NaWW4Rsx0Bbja6s4mfKu7E4cxJHhf2Xn6uBnxRO12KV9mNCZfDK5RdYoC3bnUxugYBeW5SMBEiABEvA9AVkrSOzGpCdE7MTE7lN6Q8QOUXp+ZD0dKyk/QlBkE7secWKPKTOZZaHKcHBilyc2umJDKD19YqcktmayLpLMABab3FBw7AEKhVpiGUmABEggxAjIpBbp+bG6Cxc53dVjqDKgAuSuVnmNBEiABEiABEjAkgQ4BGbJaqVQJEACJEACJEAC7ghQAXJHh9dIgARIgARIgAQsSYAKkCWrlUKRAAmQAAmQAAm4I0AFyB0dXiMBEiABEiABErAkASpAlqxWCkUCJEACJEACJOCOABUgd3R4jQRIgARIgARIwJIEqABZslopFAmQAAmQAAmQgDsCVIDc0eE1EiABEiABEiABSxKgAmTJaqVQJEACJEACJEAC7ghQAXJHh9dIgARIgARIgAQsSYAKkCWrlUKRAAmQAAmQAAm4I0AFyB0dXiMBEiABEiABErAkASpAlqxWCkUCJEACJEACJOCOABUgd3R4jQRIgARIgARIwJIEqABZslopFAmQAAmQAAmQgDsCVIDc0eE1EiABEiABEiABSxKgAmTJaqVQJEACJEACJEAC7ghQAXJHh9dIgARIgARIgAQsSYAKkCWrlUKRAAmQAAmQAAm4I0AFyB0dXiMBEiABEiABErAkASpAlqxWCkUCJEACJEACJOCOABUgd3R4jQRIgARIgARIwJIEqABZslopFAmQAAmQAAmQgDsCVIDc0eE1EiABEiABEiABSxKgAmTJaqVQJEACJEACJEAC7ghQAXJHh9dIgARIgARIgAQsSYAKkCWrlUKRAAmQAAmQAAm4I0AFyB0dXiMBEiABEiABErAkASpAlqxWCkUCJEACJEACJOCOABUgd3R4jQRIgARIgARIwJIEqABZslopFAmQAAmQAAmQgDsCVIDc0eE1EiABEiABEiABSxKgAmTJaqVQJEACJEACJEAC7ghQAXJHh9dIgARIgARIgAQsSYAKkCWrlUKRAAmQAAmQAAm4I0AFyB0dXiMBEiABEiABErAkASpAlqxWCkUCJEACJEACJOCOABUgd3R4jQRIgARIgARIwJIEqABZslopFAmQAAmQAAmQgDsCVIDc0eE1EiABEiABEiABSxKItqRUFIoE/EBg9+7dmD79EyyYNxvbt+9A9vFTqKisRHJSArqndsKIkWMx+ZLLMW7cOERFRfmhBEySBEiABEjAVwQibMr5KjGmQwJWJLBu3To8+ttfY83qVbh4UARGdC9Hj/YRaJUcgVj1CZFfDGRm27Bqjw3ztifieFEMfvvIY7jtttsRExNjRSSUiQRIgARCngAVoJCvQgrgLwJVVVV4+DcP4l9v/RP3nVuBacMiER0VUWd2mw9U4em58ciPTMHMWXPRpUuXOuMwAAmQAAmQQGAJUAEKLG/mFiIEysvLMfXi85F/cDVeuroczRLrVnxqivb+Uhse+7QUMnTWrVu3mpd5TgIkQAIk0IgEaAPUiPCZdXASkJ6fjO6p6NXyON65GYiK9F75EcmuHxmBNskxSE9Px4YNGzBgwIDgFJilIgESIIEwJMBZYGFY6RTZPYG777wDZQXH8Mp19Vd+jBwuHBiF566JxsUXTkR2drbhzV8SIAESIIFGJkAFqJErgNkHF4E5c+bgq5n/xXe/jUJkPXt+ako0ZXAULh1QgDtuvaHmJZ6TAAmQAAk0EgHaADUSeGYbfARkQmTfnt3wf2MPYUxv305jL6+0YfxT0fj0y28xfPjw4BOeJSIBEiCBMCNAG6Awq/BwF3fevJ149tkl+PrrHRg0KAVnntkR0dFRKCurUD0+JxFVccrnyo8wj1Gzx+4YU4an//4kpn82K9yrgfKTAAmQQKMToALU6FXAAgSSwLnnZiA+PlorQDfcMBj33XeOzn7nzhz06f0P9OkwSp3P9UuRpg6OxD/+PB9FRUVITEz0Sx5MlARIgARIwDMCtAHyjBNDWYhA06bxWpqIiNOzuzIyWiMyKgvHC1L8JmmT+Aj07ZqI5cuX+y0PJkwCJEACJOAZAfYAecaJoSxO4PDhHDUM1hSXjtmiJS2riMQ3m9PxwbIBuHPiKry5YAiOnkrGveevwJNfjMEvJ6zGDaM2Ytb6Hnju6xE4K/0A/jztOyzd0RkfLe+PDi3ykdYmF//8dqhaPLEK79z+Obq0ykPPNiVYtWoDPv+8QPUCxag1gk4gJaUJ/v73C5GUFGtxyhSPBEiABIKHAHuAgqcuWJIAE1iz5iDee28tHn98PsaP/xeaJe3HTaPX61JUVkXiVFE8Vu3uhM/X9MaNyn9Mr724aNBOREbYUFBaraxcfMYOdGp5SoeViH06ZmPbkdb47sc05Z+HT+7+RIWvwqvzh+l0OzQtx8svZWHkyK546qlJ+Pjjq7Bu3WFcfvl/9HX+IwESIAESCAwBKkCB4cxcgpBAbGwUKiqqcOhQHkpKylFlS8ah3Ca6pAmxFZjYd7c+njRwJ8b32YtHpi7W53ExlTg9eAbEx1TYpWuRVILOSiHqlZKDUT33o12zQozIOIjMYy10mP3Hu+HI0UhMm9Zfn4sB9s03n4lvvtmJxYsz7enwgARIgARIwL8EqAD5ly9TD2ICAwak4JZbhuKNNy7Dl19eqnZ2j8KNr18KGf4SJ4qOuGaJJfrX038m0yIdJTG2HGUqbXF7c9ojIQF65pn2UP+GD++sDzdsOGJ48ZcESIAESMDPBKgA+Rkwkw8NAhkZ3VBavhUnChMxb0t3vxU6tyhWzQKLwMmTagv5n1yfPm2VQhSJ8vJqhcvw5y8JkAAJkID/CFAB8h9bphxCBOLj49G6RUtd4jjTkJYrESqrzINgrkLV9s8rPak9V648YL8oQ3AyFNevXzu7Hw9IgARIgAT8S4AKkH/5MvUgJJCVla9LVVZ2usfl+PFClFcNQHJ8PoZ1O6Svl1dWPx5FpTEOUvTvnIXluzqjoCQGO4+2RGZ2C2TnJdnDlJZHK3ui0wpSoYovaWWdEuPp7UhLa4E5c7bbw69efVAvyihrFNGRAAmQAAkEhgCnwQeGM3MJEgLffLNDTTn/XpfmhReWql3aD2Pv3lwUFJQpW5xUbF7zOJLjSpFbmIBX5lVvWfH294PRqkkR+nWq3sz06hGbced7F2HCX2/EpWduxfDuB7FXKUGLt3dRKz5XYcvBtsp+qALr9qZoA+lF21L1FPqn5qTiiitG4N77b8CVV36Ev/xlAQYOTMH06Zvx2WfXwrwuUZDgYjFIgARIwLIEuBeYZauWgtWHwJiRQ3FR542YNrzaaNlVGmIoXaWGweJjK3Xvjig+7lxesQ0TnorE8tUbkZFR3dOza1cOoqIiVY9Q9dCbu/i8RgIkQAIk4FsCVIB8y5OphTiBNWvWYNJ5YzDrvkq0aXJ6GKuhYj34STQ6DL4GL73yz4YmxfgkQAIkQAI+IEAFyAcQmYS1CLz4wvN49ZlHMeNXFUiKa7gSdNObFShK6IXFy9YgLi7OWrAoDQmQAAmEKAEqQCFacSy2fwncctO1+HbOdHxxT4RaB6j+StDL86rw7FdlyM/PR3Jysn8LzdRJgARIgAQ8JsBZYB6jYsBwIvD2O//Btbfci8kvRGHbYff2Pc64lJbb8NaSOMzb1wVHjx6l8uMMEv1IgARIoBEJsAeoEeEz6+An8OGHH+Ceu36BSf2rcMeYSrXJqfveoNIKG2aurcQrC+IwYtREvPP+h2rT08TgF5QlJAESIIEwI0AFKMwqnOJ6TyAnJwfP/ONvasuMf6JnhxiMSC1Cz/Y2tEyKUNPdgXw1wysz24aV+xOw6McynDFoAP70t+fUhqcjvc+MMUiABEiABAJCgApQQDAzEysQKCsrw/z587FwwXz8uHkdso8dQ3lFBZo2bYKMHn1w9qjxOOecc7B8+XJcc801aoq7+6n0VmBCGUiABEggVAlQAQrVmmO5g5bA7Nmz0bNnT6SnpwdtGVkwEiABEgh3AjSCDvc7gPL7nEDv3r2xdetWn6fLBEmABEiABHxHgAqQ71gyJRLQBFJTU5Gbm4tTp06RCAmQAAmQQJASoAIUpBXDYoUugcjISD0Exl6g0K1DlpwESMD6BKgAWb+OKWEjEOjVqxd27Nih9gvzfg2hRigusyQBEiCBsCNABSjsqpwCB4JAs2bN0LJlS7XT/N5AZMc8SIAESIAEvCRABchLYAxOAp4SkF4gDoN5SovhSIAESCCwBKgABZY3cwsjAmlpaZBFFPPy8sJIaopKAiRAAqFBgApQaNQTSxmCBGQhxB49emDbtm0hWHoWmQRIgASsTYAKkLXrl9I1MgFZE2j79u00hm7kemD2JEACJFCTABWgmkR4TgI+JNC8eXOIQfS+fft8mCqTIgESIAESaCgBKkANJcj4JFAHATGG5jBYHZB4mQRIgAQCTIAKUICBM7vwI9CtWzdkZWWhoKAg/ISnxCRAAiQQpASoAAVpxbBY1iEQHR1NY2jrVCclIQESsAgBKkAWqUiKEdwEjGEwrgwd3PXE0pEACYQPASpA4VPXlLQRCciq0MnJyThw4EAjloJZkwAJkAAJGASoABkk+EsCfiYgU+K5MrSfITN5EiABEvCQABUgD0ExGAk0lED37t1x9OhRFBYWNjQpxicBEiABEmggASpADQTI6CTgKQExhk5PT9cLI3oah+FIgARIgAT8Q4AKkH+4MlUScErAMIa22WxOr9OTBEiABEggMASoAAWGM3MhAU2gdevWSEhIoDE07wcSIAESaGQCVIAauQKYffgRMHqBwk9ySkwCJEACwUOAClDw1AVLEiYExA7o8OHDKCoqChOJKSYJkAAJBB8BKkDBVycskcUJxMTEQLbHkF3i6UiABEiABBqHABWgxuHOXMOcgKwJJBuk0hg6zG8Eik8CJNBoBKgANRp6ZhzOBNq0aYPY2FgcOnQonDFQdhIgARJoNAJUgBoNPTMOdwJGL1C4c6D8JEACJNAYBKgANQZ15kkCioAYQx88eBDFxcXkQQIkQAIkEGACVIACDJzZkYBBQIbAUlNTsWPHDsOLvyRAAiRAAgEiQAUoQKCZDQk4I8ANUp1RoR8JkAAJ+J8AFSD/M2YOJOCSQLt27RAVFaXXBXIZiBdIgARIgAR8ToAKkM+RMkES8I4Ae4G848XQJEACJOALAlSAfEGRaZBAAwhkZGRg//79KCkpaUAqjEoCJEACJOANASpA3tBiWBLwA4G4uDgaQ/uBK5MkARIgAXcEqAC5o8NrJBAgAtwgNUCgmQ0JkAAJ/ESAChBvBRIIAgIpKSl6W4yjR48GQWlYBBIgARKwPgEqQNavY0oYIgRoDB0iFcVikgAJWIIAFSBLVCOFsAKBHj16YO/evSgrK7OCOJSBBEiABIKaABWgoK4eFi6cCMTHx6NLly5cGTqcKp2ykgAJNBoBKkCNhp4Zk0BtAtwgtTYT+pAACZCAPwhQAfIHVaZJAvUk0KFDB1RUVODYsWP1TIHRSIAESIAEPCFABcgTSgxDAgEkIFPit27dGsAcmRUJkAAJhB8BKkDhV+eUOMgJ9OzZE5mZmTSGDvJ6YvFIgARCmwAVoNCuP5beggQSEhLQsWNH7Nq1y4LSUSQSIAESCA4CVICCox5YChJwIMA1gRxw8IQESIAEfE6ACpDPkTJBEmg4AekBKi0tRXZ2dsMTYwokQAIkQAK1CFABqoWEHiTQ+AQiIiLAXqDGrweWgARIwLoEqABZt24pWYgTEGPoPXv2oLy8PMQlYfFJgARIIPgIUAEKvjphiUhAE0hMTISsC7R7924SIQESIAES8DEBKkA+BsrkSMCXBLgmkC9pMi0SIAESOE2ACtBpFjwigaAj0LlzZxQVFeH48eNBVzYWiARIgARCmQAVoFCuPZbd8gTEGJq9QJavZgpIAiTQCASoADUCdGZJAt4QEAVIFkWUPcLoSIAESIAEfEOACpBvODIVEvAbgaSkJLRv357G0H4jzIRJgATCkQAVoHCsdcoccgS4JlDIVRkLTAIkEOQEqAAFeQWxeCQgBMQYuqCgACdOnCAQEiABEiABHxCgAuQDiEyCBPxNIDIyUhtDb9u2zd9ZMX0SIAESCAsCVIDCopoppBUIiDH0zp07aQxthcqkDCRAAo1OgApQo1cBC0ACnhFITk5GmzZtkJmZ6VkEhiIBEiABEnBJgAqQSzS8QALBR4DG0MFXJywRCZBAaBKgAhSa9cZShymBrl274uTJk/ovTBFQbBIgARLwCQEqQD7ByERIIDAEDGPorVu3BiZD5kICJEACFiVABciiFUuxrEtAjKF37NiByspK6wpJyUiABEjAzwSoAPkZMJMnAV8TaNq0KVq3bk1jaF+DZXokQAJhRYAKUFhVN4W1CgHpBeKaQFapTcpBAiTQGASoADUGdeZJAg0kkJqaqleFzsvLa2BKjE4CJEAC4UmAClB41julDnECUVFR6NGjB2gMHeIVyeKTAAk0GgEqQI2GnhmTQMMIGMbQVVVVDUuIsUmABEggDAlQAQrDSqfI1iDQvHlzyN/evXutIRClIAESIIEAEqACFEDYzIoEfE1AVoamMbSvqTI9EiCBcCBABSgcapkyWpZAWloasrOzkZ+fb1kZKRgJkAAJ+IMAFSB/UGWaJBAgAmIMnZGRwV6gAPFmNiRAAtYhQAXIOnVJScKUgDEMRmPoML0BKDYJkEC9CFABqhc2RiKB4CHQokULyOrQ+/fvD55CsSQkQAIkEOQEqAAFeQWxeCTgCQHpBeKaQJ6QYhgSIAESqCZABYh3AglYgEC3bt1w7NgxFBQUWEAaikACJEAC/idABcj/jJkDCfidQHR0NNLT02kM7XfSzIAESMAqBKgAWaUmKUfYEzCMoW02W9izIAASIAESqIsAFaC6CPE6CYQIgZYtWyI5ORkHDhwIkRKzmCRAAiTQeASoADUee+ZMAj4nIPuD0Rja51iZIAmQgAUJUAGyYKVSpPAlIHZAR44cQWFhYfhCoOQkQAIk4AEBKkAeQGIQEggVAmIM3b17d2zfvj1UisxykgAJkECjEKAC1CjYmSkJ+I8AjaH9x5YpkwAJWIcAFSDr1CUlIQFNoHXr1oiPj8fBgwdJhARIgARIwAUBKkAuwNCbBEKZAFeGDuXaY9lJgAQCQYAKUCAoMw8SCDABMYY+dOgQiouLA5wzsyMBEiCB0CBABSg06omlJAGvCMTExNAY2itiDEwCJBBuBKgAhVuNU96wISBrAm3btg1cGTpsqpyCkgAJeEGACpAXsBiUBEKJQNu2bSHT4g8fPhxKxWZZSYAESCAgBKgABQQzMyGBxiFAY+jG4c5cSYAEgp9AdPAXkSUkARKoL4GMjAysXr0aJSUlemp8Tk6OXiQxLy8PkZGRaNWqFXr06IGmTZvWNwvGIwESIIGQJEAFKCSrjYUmAc8IxMbGIiIiAnfcdjOWLVmInOO5yOiYgKbxQHklcKrIht2Hi5DWtSOuuPoG3HLr7UhJSfEscYYiARIggRAmEKEMJG0hXH4WnQRIwAWBPXv24KFf34Uli7/HtCGVuHiQDT1Tao96SxOwcb8NMzdE47M1lbjpppvw2BN/QYsWLVykTG8SIAESCH0CVIBCvw4pAQnUIjB37lxcc+XluPHsCtwy2oa4mIhaYZx55Bba8OzcaCzJTMbsr79F3759nQWjHwmQAAmEPAEqQCFfhRSABBwJfDbjE/zithvx1k2V6N+5do+PY2jnZ9NXVuI3H5fjhx9+QJ8+fZwHoi8JkAAJhDABKkAhXHksOgnUJDBr1ixMnjwZ3/4uFmlt6qf8GGku21mJuz5QvUHLVrMnyIDCXxIgAcsQaFgLaRkMFIQEgoNAWVkFCgvLXBbm2LEC1SuT5fT61q1bcdP1V+Kze08rP/tymuF4QYLT8HV5np0RhT9dUomLLpiA3NzcWsErK6vUQovHavnTgwRIgARCgQAVoFCoJZYxbAi89946PPvs4lryVlVV4d1316Jbt6fw+usra10Xj1/dfiPumVCOQV1PP9Z3vz8JT88522l4TzwnDYrEOWkF+MOjv3UIvn79YZx55su4447PHfx5QgIkQAKhQuB0SxkqJWY5ScDCBF59dQX++c+VKJc56iYna/bceOMQDBjgfIr6woULsW/3D7j6LEdj53/d9gV+P/V7U0reH/76vAq8//57DitKn3FGB4walapWmmYT4j1RxiABEggGAmy9gqEWWAYSUASWLduH887LwJEj+fjssy1OmURFOSo4RqBXXvgHbjunVC1u6Hi9dZNiJMZVGMHsv+WVnj/6LZIicNmZUXjrzdd1fOmNEhcVFakXU9QndfyT4TKuuFEHJF4mARIIKAEuhBhQ3MyMBFwT+Pe/1+PppydhzZqDePHFZfj5zwe6DvzTFVFG/va3BZg5KxKLEu9AXvEG3D5+rb76w8E2mL6qL2KiKvHI1OphtZfnDUNK83y17k97ZOcl4rlrvkZ8bCWW7uiMj5b3R4cW+cp4Ohf//HYooqOq8M7tn6NLqzxMGViBRz78CEeOnoGOHZtqJW3jxiNISIjRea1adQBvvLEK7ds3QVxcFF54YRlmz74BXbo017L07t0WH320ERdc0AP33juyTrkYgARIgAT8TcDzz0B/l4Tpk0AYExDj5piYSCQlxeL224ep3qD9WLfuUJ1EHn54Lpo0KUD/bkvx/LXf4G+zRmHOxnQdr3liCVbs6oSTRWrZZ+VEyXln0Rm4fOhWPHH5Auw51hLzfuiur/XpmI1tR1rjux/T0KllHj65+xNERlTh1fnD9PWBXSKwY9cQjBiRgkcfHY+XX56Cffty1SrT+rIeCvviix8xa9ZWDBrUAddeOwht2iTj8ce/xalTJbj++sHKtukiPPDAbJSW1u6Rqk6F/0mABEggcASoAAWONXMiAZcE3nprNW64YYi+fumlfZXykISXXlrmMrxcOHGiCP/61xps2bIXat4XjuUlITa6AnM3VytAHVvmo3OrU/Y0OrbIw68mrrKfN0koxa6slvq8RVIJOrc8hV4pORjVcz/aNSvEiIyDyDxWvRr06syOqKzqik6dqpUX2V5j0qRe9rQGD+6Inj1bo3//9moafm88//xkZbDdEhMmpOOKKwbocM2axaOy0oadO3Ps8XhAAiRAAo1FgENgjUWe+ZLATwTEPubDDzfo3pS5c3do306dmuK//92Ef/xjElq3TnLKatOmo2oIKlptclqE9k1z1NAVMF313CTFnZ5Gb7YISm1zCjePXo8v1/fAkdwmargsDsVl1UNYkoHRm2NklhhbjrLKKH26ek9HZUuUi7KyfOMyEhNjVJzTOcTHR6Nly0T7dTmYNq2/mkJfrBSiJWpD1mrlyd00f4fIPCEBEiABPxJgD5Af4TJpEvCEwKxZ2zB1ah81qyrN/vfQQ6O1wvDmm6d7bGqmJUNLWVkFavPSSLRvnqVWfT6m/7q1PVkzqD4vLI3Bta9djibxZdpOqFVykdNwzjyPnUpGWUWS6sFxnJ1m0n+cRcPKlftVj9B7uPLKgbj11qFOw9CTBEiABBqDABWgxqDOPEnARECUnPvvPwfnnJNq/7vqqkEYPLgDXnttpVI6qmddmaLow/T0VsqephKZmTHIL4u1X56/pZuacWU/tR+8ogygxY3tvVf/evNPhs8qKuOQk3O6x8eT+Fdf/bG2BxLjaDoSIAESCCYCVICCqTZYlrAjICspy9CQs2Gun/98AA4cOKVnTxlgJGxZWXUvTN++7TB0aCfMmHEK6/d11Ss+/2fpANVTE1VrOEviH1LDXlsPt4b0BO3NbqaO26CgJFaFr24GSsujUWU7reBIOGO6/OTB21UKFZgz57guiihlYqR96FCefeVqUcYKCkr1dfknaxkdPZqPpUv36Snwn35aPbU/P79MyUBDaDsoHpAACTQKgajHlGuUnJkpCYQ5gc2bj6qhoY/0bCqZOSVGw4YTQ2GZFv/jj8f0tPhBg1KwcOEeiLF0Tk6RWhCxPdLSWmLMmDS1ZtAP2JHZBW8tHILubXNx85j1RjLK3qcn4mMqcF7/PRBD5zkbe+C/y/vpqe9npB7Fv5cORNumRSgojcW/vh+sDamHpB1BbmGCngG2/3hzDOyShbjoLMzbXoodO1tDFmuUsjRpEoeKiir1GwuZEi9l27fvpDKUboZ+/drrdYLE3kemx8+Zsx1XXz1IhTuMmTN/xPjx6WjXLtleTh6QAAmQQKAJcDPUQBNnfiTgBwIjh4/BZd3X4LKhjsNlt72tZmMppeh3k5foXKW3JyrSpv/EQ3p4YtR6P3W5575RcXrcgmeef0nNPivWPVYlJeXKAPu0EbWrNIqLy+3rBUnPkdgNycrWdCRAAiTQmATYCjUmfeZNAj4icO8Dd2L6+tqTOmVYK6PdCXsusdFVduVHPD1RfkorbPh4dRRu+8WdWnExhus8UX4kD2OxRDn2ZvVoCU9HAiRAAv4iQAXIX2SZLgkEkMDll1+OkxXNseDHSry3eKCa7XUZ1mamoFTZA03ou6dBJXl3cQQGnTEEffr0aVA6jEwCJEACwUSAClAw1QbLQgL1JBAVFYWXX3sbj34RqxYy3IoLB+7EgRNN8eqNs7XtTz2TxaYDVXh7STQunjpNbW0xW63qfHphxfqmyXgkQAIkEAwEaAMUDLXAMpCAjwg88/RTePulJ/DxLyqQHH96Rld9kt+XU4VxfynDV199pTZpPU+tOL0F69evVwbO/dR2F4PUcFb1Ion1SZtxSIAESKCxCVABauwaYP4k4GMCd995O15+9U2sejwOrZvUTwn68VAVrlGbv7/2+tu46upr7SUsLCxU09qXKkPoE2rRxlFqY9SO9ms8IAESIIFQIkAFKJRqi2UlAQ8JSE/QM39/An+6pAzj+njeU2NTKyh+tKIKz82LxdvvfqBWqJ7qNMf9+/djyZIlaip7O7VB6gi1LYbjFhhOI9GTBEiABIKIABWgIKoMFoUEfElg/vz5uOuXt6BF9ElcO6wYE/tFqs1SnfcIFZTYMHtDJd5aEo+Oqb3wyuvv6KEud+WpqKhQiyGuw9atW3HmmWdqI2nz3mDu4vIaCZAACTQ2ASpAjV0DzJ8E/EhA9u6aMWMG3njlWaxauwH9UxOR3qYCTWPLUFEVgVOlcdiVHYXtB4tw3sTx+OXdD6gd3Cd4VaLc3FwsXrxY7xMmw2KtW7f2Kj4DkwAJkEBjEKAC1BjUmScJNAIBsd1Zs2YNYmJi9GwuWYywVatW6NGjh9pSY6haryehQaXavn272vx0JdLT03WPUGzs6f3JGpQwI5MACZCAHwhQAfIDVCZJAsFIYO7cuejevbv+81f5SkpKsGrVKoiN0Nlnn6229+jmr6yYLgmQAAk0iADXAWoQPkYmgdAhcOzYMbRt29avBY6Pj8fo0aMxceJErF27Vu0BNgd5eXl+zZOJkwAJkEB9CFABqg81xiGBECNQUFCAqirZuLRJQErevn17yOrUMk3+888/18bSYo9ERwIkQALBQoAKULDUBMtBAn4kkJ2d7ffen5rFFxujgQMHakVI8hdj7MOHD9cMxnMSIAESaBQCtAFqFOzMlAQCS0CMk8X4efDgwYHN2JTb3r17sWzZMqSkpOCss85qsNG1KWkekgAJkIDXBNgD5DUyRiCB0CMQCPufuqikpqZi2rRpWvGZPn26Xj9IFl6kIwESIIHGIEAFqDGoM08SCCABUTJkCKpNmzYBzNV5VtILJb0/F198MXbs2IGZM2fi+PHjzgPTlwRIgAT8SIAKkB/hMmkSCAYCsm9XcnIy4uLigqE4ugwtW7bElClT0KtXL73L/PLly1FeXh405WNBSIAErE+ACpD165gShjmBYOn9qVkNsm2GKEBXXHEFSktL8cknnyAzM7NmMJ6TAAmQgF8I0AjaL1iZKAkED4FFixbpFZ/79u0bPIVyUpIjR47oLTWaNm2KkSNHBmzKvpOi0IsESCAMCLAHKAwqmSKGN4FgMID2pAZkdtjPfvYzyBpCn332GTZs2KDXLvIkLsOQAAmQgLcEqAB5S4zhSSCECIhdjazELHt+hYKTtYMGDRqEyy67DEePHtVrB0nPEB0JkAAJ+JpAtK8TZHokQALBQ0Dsf0T5EcUilJysWH3BBRdom6DvvvtOrygts8dkqw06EiABEvAFgdBqFX0hMdMggTAiIMNfwTD9vb7I09LStJG0zGATI+lt27aBawfVlybjkQAJmAnQCNpMg8ckYDECsgO87Mienp4e8pLJekGLFy+GzB4bNWoUZCo9HQmQAAnUlwB7gOpLjvFIIAQIhIoBtCcoZShv6tSpyMjIwKxZs7BixQquHeQJOIYhARJwSoAKkFMs9CSB0CdQWFioZ1HJtHKrOOn96dOnj95So7i4GLKlhuwxRkcCJEAC3hKgEbS3xBhIGkh4AABAAElEQVSeBEKEgJV6f2oiT0hIwLhx4/Tu8jIstn37dr12kKx4TUcCJEACnhCgDZAnlBiGBEKQQDDsAB8IbJWVldi4cSM2b96sp9D3798/5Ga9BYIT8yABEnAkwCEwRx48IwHLELByD5C5kqKiojB48GBceumlOHToED799FO9hpA5DI9JgARIoCYBKkA1ifCcBCxAIJh2gA8UTrF1mjRpEoYMGYL58+dDtgApKSkJVPbMhwRIIMQIUAEKsQpjcUnAEwK5ublISkoKqh3gPSm3L8LItH/ZYDU6OlobSe/YscMXyTINEiABixGgDZDFKpTikIAQkAUDZQsJMRQOZ5eTk6N7gkQZkrWDWrRoEc44KDsJkICJABUgEwwekoBVCITKDvCB4C3DgT/++CPWrFmD3r17a3shUYjoSIAEwpsAh8DCu/4pvUUJhIsBtCfVJ2sH9e3bV68dlJ+fr7fU2L9/vydRGYYESMDCBPgZZOHKpWjhSSDUdoAPVC0lJiZiwoQJOHjwIJYsWaKHCUeOHKltpQJVBuZDAiQQPATYAxQ8dcGSkIBPCMgO8LJPVqjtAO8T4T1IpFOnTro3SLbWmDFjBjZt2qRXzPYgKoOQAAlYiABtgCxUmRSFBITAhg0bUFRUhLPPPptA6iBw6tQp3Rsk22qMHj0abdu2rSMGL5MACViFAHuArFKTlIMEfiJA+x/Pb4VmzZrhoosu0itIz507V+82X1pa6nkCDEkCJBCyBKgAhWzVseAk4JyADIGxJ8M5G1e+6enpeu0gMZj+5JNPsHPnTldB6U8CJGARAlSALFKRFIMEhIAMfVVUVMBKO8AHqmZjY2Nxzjnn4IILLtD7is2aNQsnT54MVPbMhwRIIMAEqAAFGDizIwF/EsjKykK7du38mYXl027Tpg0uueQSpKam4n//+59eP0iUSjoSIAFrEaARtLXqk9KEOYFw2QE+UNVcWFiI5cuXQ4YVpXeoc+fOgcqa+ZAACfiZAHuA/AyYyZNAIAnQANq3tGU/tYkTJ2rlR9YOkk1WZZiRjgRIIPQJUAEK/TqkBCSgCYTjDvCBqnrp+Zk2bRpk1tj06dOxZcsWCG86EiCB0CVABSh0644lJwEHAuG8A7wDCD+dyP5hQ4cOxdSpU5GZmYnPPvtMD435KTsmSwIk4GcCtAHyM2AmTwKBIsAd4ANFujofmSq/YsUKpKWlYdiwYZBZZHQkQAKhQ4A9QKFTVywpCbglQPsft3h8fjEjI0OvHSRDYbJ20K5du3yeBxMkARLwHwEqQP5jy5RJIKAEqAAFFLfOLC4uDqNGjcK5556rtyCZPXs2ZHsNOhIggeAnQAUo+OuIJSSBOglwB/g6Efk1gKy9dNlll+lp8l988QXWrl2LyspKv+bJxEmABBpGgApQw/gxNgkEBYGcnBzuAN/INREZGYkBAwbgZz/7GY4fP65nix06dKiRS8XsSYAEXBGgEbQrMvQngRAisHHjRsiifdwBPngqbf/+/Xqn+fbt2+Oss85CYmJi8BSOJSEBEgB7gHgTkIAFCND+J/gqsUuXLtpIOjk5WfcG/fDDD1w7KPiqiSUKYwJUgMK48im6dQhQAQrOupS1g2SK/JQpU7B7926IfZAMV9KRAAk0PgEqQI1fBywBCTSIAHeAbxC+gERu0aKFVoL69OmDOXPmYNmyZSgrKwtI3syEBEjAOQEqQM650JcEQoYAe39CpqrQs2dPPSwms/Zk7aA9e/aETuFZUhKwGAEaQVusQilO+BFYtWoVoqKiMGTIkPATPoQlPnr0KBYvXgzZcFV2mm/atGkIS8Oik0DoEWAPUOjVGUtMAg4E2APkgCNkTmR22OWXX46OHTvi888/x7p167h2UMjUHgtqBQJUgKxQi5QhbAkYO8C3bds2bBmEsuCydtDAgQO1IpSdnY0ZM2bg8OHDoSwSy04CIUMgOmRKyoKSAAnUIiA7wMv6MrIlA13oEpCp8ueffz727t2LBQsWoEOHDnrtoISEhNAViiUngSAnwB6gIK8gFo8E3BHg8Jc7OqF3LTU1VRtJi+Izffp0bN26lWsHhV41ssQhQoBG0CFSUSwmCTgjsGjRIr0FRr9+/Zxdpl8IEzhx4oQ2kpZhTtlwtVWrViEsDYtOAsFHgD1AwVcnLBEJeEyAPUAeowq5gC1bttRrB/Xq1Quyy/zy5csh0+fpSIAEfEOACpBvODIVEgg4gYqKCuTl5bFnIODkA5dhREQERAG64oorUFpaqtcOyszMDFwBmBMJWJgAjaAtXLkUzdoEZNaQ9BLIGkB01iYQHx+PsWPH4siRI3pYbPv27Rg5ciSaNGnSIMF37dqlp9/Lxq0FBQV6TaJOnTrpmWm9e/eGKGB0JGBVArQBsmrNUi7LE+AO8JavYqcCVlVVQep+06ZNWlEZMGAAZDq9p+7QoUN4+cXn8Z9/vwNUlqJf5yh0bVGKxOgKFFdE48CpOPx4qApFZZG4+prrcO/9D6Jr166eJs9wJBAyBKgAhUxVsaAk4Ehg3rx5SEtLQ3p6uuMFnoUFgfz8fCxdulQPg4qRdEpKilu5xX7oj394BP989WVcfmYErhhagfR2rhWnfTlVmL4mCv9dCTzy6B/wq7vug/RE0ZGAVQhQAbJKTVKOsCPwwQcfYPLkydxCIexq3lFgsQmSzVVlRemzzjrLqZIiO9BfNvVCJJVsx+NTy9CmiedDW6eKbHhyVgz2FnfGtwuXoXnz5o4F4BkJhCgB1+p/iArEYpNAOBDgDvDhUMueySi9gGIkLYthygar27Ztc1g7qLi4GL17dkfvhC149dpyr5QfKUGzxAg8fUUFOkfvguxqz5WqPasXhgp+AuwBCv46YglJoBYBWTFYFsm78MILa12jR/gSOH78OGRtKLEJkmExWVCxV0Y3nJtxAo9Mafj37r8WVeLjTe2wet0WNGvWLHxBU3JLEKACZIlqpBDhRoA7wIdbjXsuryycKMrxmjVr8Nn0D5EeuQJ3ji31PIE6Qv7j6yjsxTDM+mq+V8bXdSTLyyQQcAIN/yQIeJGZIQmQABdA5D3gioBMXe/Tp4/eT2ztysW49ZwSV0Hr5f/r8yqQuW0NZs2aVa/4jEQCwUKAClCw1ATLQQIeEuAO8B6CCvNgf//zH3HvxHLERXtu8OwOWWFpDA7nJiMqMgIPnFuCx3//f+6C8xoJBD0BKkBBX0UsIAk4EpAd4MW2gzvAO3IJl7Pi4nK88MJStGnzpFqo8HeYO3dHLdFlYcM1azfgqVl3oNuv78N9/7kA24/Ufy+xORvTMfyPt+Hztb11XuP7RCLryEG9FlGtzOlBAiFCgApQiFQUi0kCBgEOfxkkwvM3ISEG9947Euedl4Ho6Eg8++ySWiBk77BeKT1RWVXdxD900VL0TDleK5ynHpMG7kKHFvn24DLMdtGASvxv5hd2Px6QQKgRoAIUajXG8oY9ASpAYX8LaADJyXG4/PJ++Oabndiy5agDlEXffY28gnScP2CX9k+KLXO4XteJWmy6louOrIJ5MG1YWiUWL/ymVjh6kECoEOBeYKFSUywnCfxEQBQg2SCTjgTuumsEpk/fjOeeW4K33/6ZHciGjVmqBygZ8TG194nbcrAN/vX9YLRKLlLDYq0xdcg2XD50q46rJpDhpbnD0a5ZgdoOoy2S40vx0EXL7OlWVak1gWafrYfCYqPLUBWzwH7tyJE8PP30YqxffxgnTxbj5Zen4uyzuYWGHRAPgo4Ae4CCrkpYIBJwTUB2gD916hR3gHeNKKyudOvWUq0G3gsffLABWVmnh6gOH+mMacM21GKRmd0cN795CX5/yfd4ZOpi/PHShXh0xgR8vKKvDrtkRxfkFcfh52f9gD+oaxU/DaEZCX21KR1jeu/FF/d9hKgIG44d76MvVVRU4uabP8UTT5yLb7+9VW3UmoopU97HiRNFRlT+kkDQEaACFHRVwgKRgGsCsgN8q1atuAO8a0Rhd0XsgUpLK/HKKyu07MeOFaiVoKPRuslphciAIr07Q9MOo0VS9dT47u1y1fkhPPPV2TrIodwmmLelOw4cb6pme9nw8+E/GFH17/i+mRja7TDaNC3CGakHUVXVQvt/+OFGVFRU4csvt+LjjzepXeVjcfx4kVqUMdMhPk9IIJgIUAEKptpgWUigDgK0/6kDUBheHjeuO/r3b4fXXlsBmSH2+usr0blzFnILa8PYfKCtUl4cLwzqehQ5+UnIzkvEBf13QY2CYdLT1+LdRYPQrW1u7UR+8omOLFULIVZvjrpy5QF06dIMGRmt9d+0af3VQox34ZxzUl3G5wUSaGwCVIAauwaYPwl4QYAKkBewwiio9ALl5BThrbdWY/HivRg+rD12ZtW2ZK6oisKeY9W9Ngae3h2y9WF5ZSSaJ5Xif/d/iGHdDuGJL8bi9zPGGcFq/eYW2tTGq3Ha/9SpEpSUVGDIkI4Of61bJ9WKRw8SCBYCVICCpSZYDhLwgAAVIA8ghUmQKjVVq7KyWsm55ppBaNkyAQ8+OFv1wNgQGZOIFftqKx892udg4/52aojsNKSDJ5qiSXwJUpoXYNXujloJevu2mXh4yiJ8sGwAjp6snY7EPnQqGslNmuqE0tNbYc6c7cjLO73qtNgFzZ697XRGPCKBICNABSjIKoTFIQFXBGQH+PLycjRtWv3ScRWO/uFBQIye16zZhoULF2LGjI/V5qdNEBMTiTvvHIO//OUvWLy1FKeKYzSME4WJ+vf2cWuRXxKPNZkd7JA2HWiH28auU4sqAst2dsa+nOpNTm8evU7ZERWhaUL1PmIFpbEoLInV8corbdiVFaXyq1aOrrvuDKX8lOLKKz9CZuYJbN+ejQcemINhwzrZ8+EBCQQbgajHlAu2QrE8JEACtQkcOnRI2XgUqy/8jNoX6WN5AtLjk5WVhXVqJ/Z77/0UX321X63EfFjZ/3RSM67G4txzz1BGyTZcffVZygg5CZ/O2IcVm9uivDIWWaeSkNH+BPp3PoaOLdR09dkjtSH0aqUIFZfF4P4Ll2uj53V7U/DWwsFoppSeFbs7Y3j3QxiSdgSzN2Tgk5X99AyxIamH8d7iBGw5MlrN8qrCwIEpGDGiK1JTmytD7OV45pkl2hD6wQdHYdCg04qW5SuIAoYcAe4GH3JVxgKHKwHuAB9+NV9QUIADBw7g4MGDEAW4SZMmysC5Mzp16oT27du73Y199+7dGDakP766v0oZPpuXMATKKiKxO6slOrbMt/fwCN1Ktc5PpJrenpOfqHt/pFeopssrtuH8Z6Lw9bdLlYIzyOFyeXmlKu8pdO3aXM1U5ACDAxyeBB0BKkBBVyUsEAk4JyC7bw8cOFC/AJ2HoG+oE5B1ng4fPqwVHlF6SkpKtLIjCo8oPrIHnDfuyScew5cfPIt/31qGWB9siqp7mN6IwjkX3oxnnn/Jm6IwLAkEHQEqQEFXJSwQCdQmIDvAv/vuu7jqqqvUzJvqqce1Q9EnFAmcOHHC3ssjQ1xt2rTRSo8oPLLmk+y71RA37bLJyN4+H2/dbNM7udc3LbkH73gX2FeUgq3bd3EtqvqCZLygIUAFKGiqggUhAdcEZAf4b775RhmZXuk6EK+EBAHp1ZHeHfmT4a2YmBh7L0/Hjh31uS8FkV6lgf17I3PPbqx7MhZxMd4rVBXK6PmeD6NxuLQDVqzZ6HVPlC/lYVok4CsCVIB8RZLpkIAfCWzfvl3bgIwfP96PuTBpfxAQ42VZvsBQeE6ePIkOHTrYe3kCMatPem/uu+dXmPXZf/DYlFKc06P2HmGuZF+bWYXfz4zF8DEX4867f429e/di6tSpiI2tnhHmKh79SSDYCVABCvYaYvlIQBFYvHgxWrRogX79+pFHCBDIz8+3Kzxi0+ON8bI/xZs5cyYe+vVdaB6Tj2lnFGJs7yhl7Fy7R0gWOVy0rQqfbkzCgZOx+OtTz+vhVynbsmXL1KKLOZg0aRKio7mftj/ri2n7lwAVIP/yZeok4BMCn376qVrnZRTatm3rk/SYiG8JmI2XZVirtLTU3sMjBszeGi/7tnSOqUmP1Jdffon3//VPfLdgEeJVR05auzgkxUWgqMyGfcfKUFBchdGjRuCaG27HZZdd5jAsJ71J3333ndr7q0JNvT/X7Uw0x5x5RgLBRYAKUHDVB0tDArUIyIvmvffew4033kjD01p0Gs/j+PHj9l4eGeIS42VjirovjJcDJdm+ffv0sFZhYaFeP0hkSE1NdavYiBL11Vdf6Z6t0aNHB6qozIcEfEqACpBPcTIxEvA9gSNHjmDlypW45JJLfJ84U/SYgCvjZVEYxKZHjJnDycmq5NKTJPIPHTo0nESnrBYhwAFci1QkxbAugezsbN27YF0Jg1Myw3jZWIjQMF6WF/7gwYPDfksSUfjEDuiLL77QQ3y0TwvO+5ilck2ACpBrNrxCAkFBQIZXunbtGhRlsXohxHjZUHjEeFlmaIkNz/Dhw+tcednqbJzJJ2tSXXzxxVoJkuP09HRnwehHAkFJgApQUFYLC0UCpwmIAsQhhtM8fHlkGC8bSk9ZWZlWeNLS0rTReTAZL/tSbl+mlZycrHuCZKVyUYJEYaQjgVAgQAUoFGqJZQxbArIDvLyUmzWr3qE7bEH4UHAxXjYUHlEuZWadvLQnTJjgk5WXfVjUkEmqZcuWOO+88/RinTIsJgbhdCQQ7ASoAAV7DbF8YU1AXtDt2rULawYNFd6Z8bLY8fTv3z8sjZcbytNVfNmcdezYsXp2mCyUSKXdFSn6BwsBzgILlppgOUjACQHZAT4yMhJnnnmmk6v0ckZAjJdlTy1j5eVTp05pRUeUHunpCcTKy87KFS5+smr52rVr9azFxMTEcBGbcoYgAfYAhWClscjhQ0B6gAYMGBA+AtdTUrPx8qFDh3Tvgyg7I0aM0D1ookTSBYZAz549UVxcjNmzZ2PKlCmIi4sLTMbMhQS8JEAFyEtgDE4CgSIgK+7KFHiu/lybuKxBI7O0jF4esZOSHp5u3bpBFuYTY1y6xiMwaNAgiP2abODLLTMarx6Ys3sCHAJzz4dXSaDRCHAH+NPoRRk8ceKEU+NlUXxk5WW64CIgdbZgwQKIssotM4KrbliaagJUgHgnkECQEgj3HeBlGEWGs4wZW7Lwnig78icrL3MjziC9cU3FEnusr7/+Wm+xMWbMGNMVHpJA4xOgAtT4dcASkIBTAuG2A7xhvGwoPHl5eVrREVseUXpkR3W60CMgPUCyRlDHjh0xbNiw0BOAJbYsAdoAWbZqKVioExADaDEotbITJcew4xGbHpk6LcoOjZetU+vSc3fhhRdi5syZkFlh3DLDOnUb6pJQAQr1GmT5LUlAViiWvaesZttiGC8bvTxyLgpP9+7dIUMkNF625O2s6/Wiiy7ilhnWrN6QlYoKUMhWHQtuZQI5OTmQ1XWjoqJCWkwxhJWVl6WXR/6MlZdF6RHDWKspeCFdWX4uvGyZIUqQ7CDPLTP8DJvJe0SANkAeYWIgEggsgU2bNkHWthk5cmRgM/ZBbmK8bCg80tMj68AYdjw0XvYB4BBP4ujRo/bp8dwyI8QrM8SLTwUoxCuQxQ99AmL8KwqDrFgsC/ZJr8jmzZuRmpqKjIyMoBdQyi8vNUPpofFy0FdZoxdw//79WLhwoV4osXnz5o1eHhYgPAlQAQrPeqfUjUxAFjj8z3/+jS9mfIA167agRZNYNEuKRkVlFU4WVKC4rBIjhg/FVdfdgiuuuEIbjzZykR2yFyXHsOMR42V5iRm9PLJwI1dedsDFEycEduzYgTVr1kD2DUtKSnISgl4k4F8CVID8y5epk4ADAbHt+dPjf8D777+L8X2jcHG/UgzrHomE2AiHcKeKbFi2swpfbk7Ayt2V+N3Dj+Kee+9vtG0FXBkvi9IjfzRedqg+nnhIYMOGDdi5cye3zPCQF4P5lgAVIN/yZGok4JKANPaTJ52LsRnFuGdCBVo3cVR6XEXcc6wKf/0qDrnohP/NnqfXU3EV1lf+ZuNl6ekxtuQQ42X5EwNtOhLwBYHly5fr+4tbZviCJtPwhgAVIG9oMSwJ1JPA0qVLMeWi8/D0FeUY27t+M7temGvDC1+X6j2wUlJS6lkS19EM42VjaEuMl0XZkR4eGi+75sYrDSPALTMaxo+x60+AClD92TEmCXhEQOwchg4dihn3xGJwasN2Jf98TSX++nU81q7fgi5duniUv6tAZuNlUXpk1pkoOkYvj0xbpiOBQBCQe5FbZgSCNPMwE6ACZKbBYxLwMQGZGTVsyAA8fEEBLhpUv56fmkX61yIbvtzZBctXb0RCQkLNy27PDeNlUXiOHDmijZeNXh4aL7tFx4t+JiCLf8oaQdwyw8+gmbydABUgOwoekIDvCVw65UKkVizEPef6Nu27P4jG4AvuxhN/+ovbhMvKyvSQmTFFXV4yxmwtedHQeNktPl4MMIGSkhK9ZUafPn3Qv3//AOfO7MKNABWgcKtxyttgAvn5pcpos1ANFTVDTEyUGjoqVYpEtD42J75u3TpMPn8UFvymEjFRnhk8m+O7Ot55VK0QHZmPaa9WYlfmAQeDZMN42bDjEePldu3a2ZUeGi+7okr/YCFQUFCglaDhw4cjPT09WIrFcliQABUgC1YqRfI9gUq1Ps/TTy/GjBmbMWhQilqksIWavnscO3bkICenEF99dZPaz6qVQ8Y3XPtzdCn8HDeP9s3Ql5H40D/cjutGbsTRwrUYPPn3uOvuu+1r8khPj7HNAI2XDWL8DTUCubm5ejhs3Lhx2iYt1MrP8oYGASpAoVFPLGUjEigtrVAbdb6B8vJK1SjfoAyFm9pLs3HjEUyY8Ba+++5WDBhwemaWDDW1atEE3/2fDS2Tfdf7IxlnnUpC86QSbNxXjifmtsdDDz+u7SaMoS0aL9urhwchTCArK0sbRstO8mKfRkcCvibAzVB9TZTpWY7Ak09+h5UrD2DFil86KD8i6MCBKfjNb8agsLDMQe7Vq9eiS9t4pfyUOPj74qRds0KdzJC0COw9cASXXXYZmjY9rZS5ykN6sSIjIxAR4VuFzFV+9CeBhhCQoVvpAfrmm28wefJkbbDfkPQYlwRqEmAPUE0iPCcBEwGxqUlO/iO6dWup9ue6z3Tl9GFZWQUqKqrUdhWxmDdvJ9atO4z169dh3uxN+Pcd89G7Yw427GuH/67ojzZNChEbXYl3Fp2Bl6+fjd3HWuKzNX308WOfj8UPB9viptHrcc3Zm/DsV2fj603p6NY2F/+86UskxlVgbWYKZqzug6YJpfjd5CW44LnWGD72CVW2fLz77jTcc8//1HEWnnlmEm666UxdyCNH8vDii8vQu3dbfPTRRlxwQQ/ce2/obbJ6mjiPwomAbJmxevVqXHLJJdwyI5wqPgCyNmxRkgAUkFmQQGMS2Ls3F0VF5UhLa+GyGLGx0Vr5kaGyG26YjuHDO6t1f6qQ2joTL84druNFR1Vh7ubu+PbHbkohysYlQ7apvb9K1HBWMjYdaIcv1/fE89d+jUenLsJfvxyFp2afgxtGbcDz132FLUopmrmul06nffMCLN/ZGUWlMfq8bbNiZYRdiK1bj2HBgt3KePR6XH31QNx//yx7eR9//Fu10WoJrr9+MJ599iI88MBsSFnpSCAUCPTo0QP9+vXDnDlz1H1bGgpFZhlDhAAVoBCpKBazcQgcPZqvM27ZMrHOAsTGRqlelyE444wOyl6oXO3vVYqdWdWG0f06ZeuenF4pOZjYNxN/uPR79FU9Q2d2OwybLQI3jlqPpLhynNt/t8onAiN77EfnVnk4M+0I+nU6hgPHm+n8O7bIR0rz6jKJR0JsFbp2TVAvhkrcd985apggAdOm9VcKTymysqrDTZiQrjZUHaDjN2sWj8pKmzLgztHn/EcCoUBg4MCBeiajLJYo9nV0JOALAlSAfEGRaViWgAx9iTtw4GSdMoptzZ//fD4yM09AbW+EQ7kd1K7u1T01EjlODX01S3S0CYqAzSHdGNVTFBvl2MAnx5chtyjeIZxxUlASUWstn6SkWH25pKQ6HVGIxFbp+eeXqE1Y1+lrNW2WjPT4SwLBSuCss85CkyZNMH/+fMjK0XQk0FACVIAaSpDxLU2gXbsmagZKEn788Zj68qysU9Y//ek7vPHGKvzyl4MQFZOl+nIcFZw6E1ABIpw9laqXyJnbk1VS5wyZlSv3KyPS93DllQNx661DnSVDPxIIegLygTF27Fit/CxatCjoy8sCBj8BZ01t8JeaJSSBABJ4+OFxOHq0QBkZV/ee1MxaDKVlMcR16w7h97+fhyefPFfZAQ3HwezSeqg/NVN3fb43W83qiopzWAjRWeirr/4Y1147CO3bN3F2mX4kEDIEIiMjcd5550HWCVq1alXIlJsFDU4CVICCs15YqiAi8KtfnYVRo1LVdPev8P33exxKJsbFYnB87FgB9u+vHiZbunQf4uKSlGF0f+QVx6CsIlJ9tUL9RtmNl41EyiqrF0mstFU/ikqXqhWuXMWrNPUAFZbGQv7mbrHh4osno6SkXCdnDAsUFFRPyZd1i+RP7JikTKKoffrpFh02P78MMnuNjgRCjUB0dDRkbaDMzEw143FzqBWf5Q0iAlGPKRdE5WFRSCDoCERFReK6686AKBa//e03erbVpk1H8be/LVQLIO7BI4+MU9PkW0FWh54zZxtee20ldu06jlGjm2DJ0nzsymqD4vIYfLKyLw7nNoXM5OqZclwvaPjyvOHYk90SYufTq0MO3lgwBMt3dVGKUzyGpB7G5oPt8O7iQThysokyhs7Cyt2dMH11X5woSMC6A6fw4G8fwZtvbsW+fSfVWkDxeqr7Y4/Nxw8/HIOUe+LEdKUgVehhuTlztqsZYoOwceNhNVvsR4wfn662yeCO70F3w7FAdRIQJSg1NRUyFCYbArdqVT3ZoM6IDEACJgJcB8gEg4ck4AkBmRovM6wyMlqr4afas8OKi9UMsIQYPWW3W9dOePHneRic6tvO1o+Wq2n1hwdh4ZKVnhQZRpkksCyIqMwp1KKIvi2TRwVhIBLwIQFumeFDmGGYFBWgMKx0ihw4AtOnT8dv77sZM+8uV9PcnRsye1uaA8ercPkr0Zi3YKnal2yQt9EZngQsRUC2zJDVoi+44II6JwRYSnAK02AC/ARsMEImQAKuCUybNg2XX3ULbnw7CuVq/Z2GuuMFNoz5cxmeffE1Kj8Nhcn4liAgW2bI7DBZI+jkybqXq7CE0BTCJwSoAPkEIxMhAdcE/v6P59C0wyD0fKhUGUHXXwk6dMKGKc8Dj//xEWWTdL3rDHmFBMKMQJcuXSDrBM2ePVvty1e9V16YIaC49SBAI+h6QGMUEvCGwL59+9CydVu0b9sSf/tgKwZ3qUCbpt4Nhy34sRK3vRuJX//mMTz86B+8yZ5hSSAsCBiG0MvVKqTp6ekQQ2k6EnBHgDZA7ujwGgk0kMDhw4f1yrWTJk1C69at8d577+E3D96LEd2rcN3wUpxRh3H091sr8daSeBwrbYann3tFr38yZcoUtGjhem+yBhaZ0UkgpAmsWLFCLf1wVC0RcTGVoJCuSf8XngqQ/xkzhzAlkJOTozdwnDhxIjp06GCnkJ+fj9f/+RreeO1FFBWcxFnpUUhrUaS2yQBksencogjsyU3C8u2l6N69G351z0O45pprdGMuO2Nv2LABl156KWJiTm+zYU+cByQQ5gRkvasFCxaoda7K9KKJnO0Y5jeEG/GpALmBw0skUF8CYoz55ZdfYvTo0Wqz0q4uk9m5cyeWLFmCHdu34WRutpqaHoXWbVPQq1dvtfjiKL0BZM3ICxcu1NsBjB8/vuYlnpMACSgCsiioGEUnJiZqA2lCIQFnBKgAOaNCPxJoAIGCggK10OBMDB06FD169GhASs6jym7Yn3/+Ofr3768UpV7OA9GXBMKcgDwns2bNQkpKit6aJsxxUHwnBDgLzAkUepFAfQmUlJTomSgDBgzwi/Ij5RLjznPPPRcrV67E8ePH61tUxiMBSxOQ50TWBpJJCJs2bbK0rBSufgSoANWPG2ORQC0CYnMwZ84ctS1GN907UyuADz2aN2+OkSNHYt68edrWwYdJMykSsAyB+Ph4yAQE2TNMhpvpSMBMgAqQmQaPSaCeBCorK/VqtG3bttVDX/VMxqtoMtW3U6dOaoPW772Kx8AkEE4EkpOTtRIk0+MPHDgQTqJT1joIUAGqAxAvk0BdBMTgcv78+drgUnplAulGjBiBvLw8bNlSvct7IPNmXiQQKgRk2Yjzzz9fbV78HY4dOxYqxWY5/UyACpCfATN5axOQKbeyI7X0AI0bN05tMurdAocNpRMVFaXtgdauXYvs7OyGJsf4JGBZArJlhjyj3DLDslXstWBUgLxGxggkcJqALLp26tSpRl1vpGnTpnq6vdgDlZaWni4cj0iABBwIyJYZ0mvKLTMcsITtCRWgsK16Ct5QAuvXr8fBgwf1TJPGXnY/LS0NqampegG4hsrF+CRgZQIZGRl6koIoQfxg+P/27gM+qirtH/gvhQBJCDUQSuiQICAl9N57aLq6KrjW1RVXF8u7+q66/pfXXfVVXPu+dlEsq7DSW4BQpJeE0EJoAUILIaSXSfmf58YbEkhiyuTmzMzvfj44k5l77znne2LmmVOduaZ/vWwMgH7diGdQ4CaBI0eO4OjRo5g8eTJq16590/s18YJsBpmRkcEpvzWBzzQdSkCWqZDWoFWrVkHWC+LhmgIMgFyz3lnqKgicPHkSMuZGgh9ZaVaXQ5b8l/WBZKsM2QuJBwUoULpA//79Ub9+fWMCg0xk4OF6AgyAXK/OWeIqCEiXl2xdIWuLyNgb3Q6Z8jtixAisX78esigjDwpQoGQBmbAwfPhwmBMZSj6LrzqzAAMgZ65dls2uApcuXTKm0Y4bNw6NGjWy673teTNp2pc1gmTKr/xx50EBCpQsYLaayt59srI6D9cSYADkWvXN0lZS4OrVq8ZChzKNNiAgoJJ3se4y2YfMZrMZ3WHWpcqUKOB4AjKBYeLEidwyw/Gqrso5ZgBUZULewNkFUlJSjC0uZJHDwMBAhyiufLMdM2aMsUDi+fPnHSLPzCQFakpAJjLImD5umVFTNVAz6TIAqhl3puogAunp6caO0r1790aHDh0cJNcF2fTx8TEWfpOuMCkHDwpQoHQB+f9FgiBumVG6kbO9wwDI2WqU5bGbgLm5aVBQEG655Ra73dfKG8leYcHBwRwPZCU603JYAdlkWHaQ55YZDluFFco4A6AKcfFkVxGQtUFkjZCWLVtCWn8c+QgJCTGyL1P3eVCAAmULyIbGo0aN4pYZZTM5xbsMgJyiGlkIewrImiCyrYRMc5fFBR39kOm+o0ePNhZulGn8PChAgbIFZKyfuWVGampq2SfzXYcVYADksFXHjFeHgEwb37hxI2QQsawRYvXmptVRJrln3bp1jSBImvbT0tKqKxnelwJOI2BumbFy5UquqeU0tVq8IAyAinvwJxcX+Pnnn40Bw9JiIkGQMx3Nmzc39kAKCwsDV751ppplWapLwNwyQ3aQ55YZ1aVcc/d1rr/wNefIlJ1AYM+ePbh8+TLGjx+Pmt7ctLo4e/bsiVq1amH37t3VlQTvSwGnEpBucG6Z4VRVWlgYBkCFFHziygIHDx7E8ePHjQXRvLy8nJZCuvRkgKeU9cyZM05bThaMAvYUKLplBldXt6dszd6LAVDN+jN1DQRiYmIQGRmJKVOmGGNlNMhStWahTp06xiKJ4eHh4ADPaqXmzZ1EgFtmOElF3lAMBkA3gPBH1xKQVhBZ+EwWQJONRF3laNasGaQ7TGa7cTyQq9Q6y1kVAXPLDPmbceDAgarcitdqIsAASJOKYDasF7hw4QKkFUT2AZIF0FztkAGe3t7e2LFjh6sVneWlQKUEuGVGpdi0vYgBkLZVw4xVp8CVK1eM1g+Z7eXv71+dSWl97xEjRuD06dM4deqU1vlk5iigi0DRLTM4jk6XWqlcPhgAVc6NVzmwQFJSkrHK89ChQ42Vnh24KFXOunyjHTt2LDZv3ozk5OQq3483oIArCJhbZsiaYZcuXXKFIjtlGRkAOWW1slClCcgigCtWrEDfvn3Rrl270k5zqdelBaxPnz5Gi1hubq5LlZ2FpUBlBcwtM9asWYPExMTK3obX1aAAA6AaxGfS1gpkZmYawU/Xrl2NDUKtTV3v1MRE1jrZtm2b3hll7iigkYC5ZYasFs0ZlRpVTDmz4qbWNMgv57k8jQIOK2Cz2bB8+XKjy6tfv34OW47qzHh2djYWL15stAZ17NixOpPivSngVAJRUVE4fPgwpk2bBllm4sZDPmZllfnw8I04ErVPLbh6ATm2HLXfYH10Cu6OgYOHGktTyJcQHtYJMACyzpop1ZCAdOvIzu6yuemwYcNqKBeOkWxCQoIRKMofclecGecYtcRc6iiwc+dOyMxSWU/MXEk+JSUF7737Nt575y008c3DoHaZ6NQ0F418AC9PIDULOHU5H7vO+mDPiSxMGD8W//3iPPTo0UPHIjpdnhgAOV2VskBFBeSbl6x1Iysgjxkzxmk2Ny1aRns/P3r0KOQb7YwZMwr/kNs7Dd6PAs4oIIOipatdttORFuffP3gvBnfMxaPDs9GxWdkjTlIy8/Hj7nx8tMkTT859Bk//11/gzKvS61D/DIB0qAXmodoENm3aZPTNT5gwAR4eHtWWjrPdWHaNl9VvZZo8DwpQoHwCsqioDIpe9OP3WL3sB7xzVw56tS078LnxzqkqEPrrEi9czOuA8K07GQTdCGTHnytWM3ZMmLeiQHULSJP01atXMW7cOAY/FcSWrkLZGDY6OrqCV/J0CriugHxpOH/2JFYv+RaL5uRWOPgROd86bnjzThsuxR6ALFMhf8N4VI8AA6DqceVda1hA9vaKjY01VnmW3c95VExAxjDI+kCySjT/AFfMjme7rsCnn3yChx55HMv+5Iamfm5Vggh7zgsPjKyDGaETkJOTU6V78eKSBRgAlezCVx1YQMawHDp0yNjfq6QZGQ5cNEuz3rBhQwwaNMgYQyWz6HhQgAKlC8iXrueefRJbX6yNBj5VC37MVF4IVS1CWUfxrw/fNV/iox0FOAbIjpi8Vc0LyJYOW7duxdSpU411bWo+R46fA1klWgIg2TaEBwUoULLAuFFDMbTxLswaZN92haup+Rj7hjv2RR5G27ZtS06cr1ZKwL41Vaks8CIK2EcgLi4OW7ZsMbq9uJ6GfUzlLtIKJCvdyjonPCjgygKpat56XFzSTQTS+nMoaj/uGlCxlh81ZhonLze86X7mC7l5bjiX2Ay/7Q+89cZr5st8tJMAAyA7QfI2NSsQHx+P9evXG+NWmjRpUrOZcbLUzfFAe/bsgWwiy4MCzixgs+Xi/fe3IzDwH2rZjOdx++1fq73yTuGLL/aiceN5+OGHqJuKv+CLT/GbPjnwcC9/ACSBz8y3f4s/Lph40/3MF/aeaoHpb92NgR3q45tvF4Jb1Zgy9nlkAGQfR96lBgWuXbtmLHQoU7abN29egzlx3qSlRW3IkCHGeCBZMZoHBZxVoFYtD8yZM1Ctg9XVKOIrr4xXC6i2w333haBRo7olFnvNyqUYHlSxTRXaN03EyFtOlXg/88V+HeKw6S+fYUjna2isxhXt37/ffIuPdhBgAGQHRN6i5gRk/x3Z3HTgwIFo3bp1zWXEBVJu3769YRweHu4CpWURXV2gXr3aBoH5KD94errftJiqfCE4fvocbg0su/VHurNu3HjKw+3Xg6bAxslGPnq3ycfevXuN5/yPfQTUYtw8KOCYAhkZGcZqq7JsfKdOnRyzEA6W6wEDBmDJkiXGStHdu3d3sNwzuxSoukBaWjaeemo5Fi6MUHsL+uHll/uidTNfHDjbAN/t6A7/emlqm4tcfL65Fz57eAlaNEzGF+p5x2ZXsXR/EIYHn8b9wyKKZSTsYHt8sL4vYq80wHNTtuA3/Q8jy+aB1Qc64oddXfHHsTvRpkEsdu86hpiYFaorLB/Dh7fD3/623gjI3nknFL6+tTFv3nq151is0YL1wgujiqXBH24WYAvQzSZ8xQEE5FuX7MAsm3Z269bNAXLsHFmU1bRlfSBpipeFEnlQwNUEvv02EhMmdFYbB89CSko2Hn00DN61PeHpkYe1UR2w/nB7dGkZj+khR9HINx1vrxmAlEwvzOx7BC9M24xXlgxDVs71VenPXfXDxSRfLHhkMUJ7RWPeT8MNUmkxalwvHdtiWiMv3w0+ao/VzIxkbNp0CkuXHlGLu7qrdboeQ1CQP2bP/jfCwo7j009vxxNPDMKLL65T63elu1rVVLi8DIAqTMYLalpAFgVbvXo1AgICjJ3Lazo/rpZ+vXr1jE1lZY+1rCy1myMPCriQwO9+11utLt8Zgwe3xauvTlAboKYjIakFurWKh4zrCW5+BWO6nsJLMzahteq+GtzpLCb3jDGE6tXJUsGMO07HNygUa1IvA7MGH1ArQNsw8dYYtUFqbVxO9lZBVQ66tLg+6SBDDb0LCPBG9+4B6NLFH6GhXdQ2GZ7qMRjnziXh6aeHqqU/6hitP3Lz06cTC9Pgk5IFGACV7MJXNRWQvXbCwsJUc6+vMT1b02w6fbZkPZIOHTpANn+UDWd5UMBVBIqOCRowINAo9uXEgsHRtVXXV33vzGIUk1Tw06VFPD7b1AuL93Qx3svILnl1+np1CyYYpGV5GeepUUOF9zqfVAstWrVRXV4oNg5Jur6KHr6+BdcmJLAFqKhLSc8ZAJWkwte0FJAPWtncVB5lxpfs8M6j5gT69etn7Hwta6DwoICrCaSkpCAt7RLq1vVQe3blIu7q9WClqEVEbAAe+mSa0b11Z/+DRd+q0POYK3XQtWvBzLSiF7qrqfdF/xYWfV70PD6/WYAB0M0mfEVTge3bt6s+9xRjDIpsOsijZgWkDsaMGYMDBw7g4sWLNZsZpk6BahSQL12yJ57NlgPZamfhwoX46aefsHt3NDIyclVrdFtsjs4tMQdPfjUR0/scgb9f5VtkMm35OHAqXXW7DS4xDb5YOQF+ilTOjVdZLLBv3z6cP39eDT6coKaicvKixfylJiddkSNHjjS6JTMzizf9l3oR36CA5gLS1R4bWzD+5qef1uLLL7/E2rVrjYUIfXz8MGXKFDXweDbS05th1qyeePyJ+/CfSB9kq8HN6VnXu7dsue6IT/GGLGgoPcWrDxTMVk1V52TnuCNNdYWlFTnffG52kZldYeFHPTF61Aij6z8rK1dtjqqWkP7lyMzMMX7OyirYMNV8LHqOeS4fiwt4vKyO4i/xJwroJSBbMMjmpqGhoeDmpnrVjeRGFkmU4OfgwYPGcgRsgtevjpijsgVkYsWFCxfUFPMY7Nq1R00vX6EWVz2nfq/z1L9aanudIZg2bSSys/Pw738fVa0++WrbnVM4ezYJ//znFAQHB+GleStx5nJnXEj0Q0CDVAQ1T1ArQ+cjXQU5326/FRuPtMW03tE4fN4f6w52QEs1PV5eP5PQAK0aJamfU/Du2gGIvtAEuWrWV7AaAP1peAgizwQg5lJtvPT/5iA52VMNvA5X23EkqwkgLY1CzZu3QeU7wQiwQkJaYv78rQgPP6nybVMtRm3g56emj/EoUYCboZbIwhd1EThx4gSk62vatGmQ2Uc89BSQb8zLly9Hq1at0Lt3bz0zyVxR4BcBmb0o3bYS9MijdG81btzYmFkqq8nLDFMvr4LBxCWhXbqUgiZNfIyp6Ob7ixYtwl/m3o+lT2Sjlkfx8YmZ2R6o41XQRSbT22Vwc3l78T/cABzOGYxlK9cVG+tjpsvHygswAKq8Ha+sZoGzZ88as4ykublRo0bVnBpvX1WB9PR0yIfAqFGj1AJxBd9Oq3pPXk8BewikpaUVBjsS8CQnJ6NZs2bG1jkS7DRt2tQuXev33nMnrkavxDt32+wSrHy3PQfvhPtib8QhtGjRwh4UvEcRAQZARTD4VB8B+SMlfe7jx483/lDpkzPmpCyBuLg4bNiwAbfddhu8vb3LOpXvUaDaBJKSkgoDHmnlkYVTzZYdCXhkw+TqmEgh6Qwd1Adtah3Da3cUbwWqaGG3HsvFvf+y4ciRI6qLLbiil/P8cggwACoHEk+xVkCao6U7RVoSpEuFh2MJyH5FMmB98uTJ1fIh41gazG11C8gMrYSEhGJdWhLcmAGPPDZs2LC6s1F4f5vNhqb+TdDR34YvHsxTCxpWPBBauj8P85Z5YcXqMPTv37/w3nxiXwEGQPb15N2qKCBN00uXLjUWOZTNN3k4noB8IMk2JdKt0LdvX8crAHOstUBubi7i4+MLW3iktdjHx6fY+J2aHi8og6ofe/RBrFjyI56baMOUXte3vigL9+TlPLy5rjZOXGuI5avCuMdhWVh2eI8BkB0QeQv7CMgYEtlos2fPnmqp94IVU+1zZ97FagHZqFbGAw0fPhyBgQWr5VqdB6bnHALSonLp0iUj4JHurCtXrqBBgwbFWnh0nR26efNmPP/sE4g7cwKht9owpFM+OgW4oaFPQauQTW1qevJyPnaeyMO6aB8cu5iPp5/5M554cq5aYLFgdWnnqEU9S8EASM96cblcyawMafmRXd0lAOLh+ALmOK6ZM2ca65c4folYAisEZEkFCXTMGVrXrl0zxuxIV5b8k8HLtWpdX2vHijxVNY2IiAh8/903CF+/CtHHTiIxOV3NCvNU+4Llo13r5hg4aAimzrgDkyZNUqtKF9/aoqpp8/rSBRgAlW7DdywSkOZiGfMjgxMHDBhgUapMxgoB+cN/+vRpTJ06leOBrAB3wDRSU1OLBTwyY0v+FphjePz9/dV08/J1ITlK8WXZCGnZYrBTszXGAKhm/V0+dflDIDu7y4wh6S7hInrO9Ssh44HWrFljdFkwuHWuuq1saRITEwtbd6SVR8b0mK07EvjIejz8O1BZXV5XEQEGQBXR4rl2FZAPx/Xr10OCINlTqjqmpdo1w7xZpQSkS2Px4sXGwHbZRZ6H6wjI/9syZke6Q81uLWn1MFt35FFWEudBgZoQYABUE+pM0xDYsmULZL2OiRMnOl0TN6u4uMDly5eNlr4ZM2ZwRe/iNE71k3RnS12bAY8MXvbzU1tD/NKlJQEP14dyqip36MIwAHLo6nPczO/evVvto3PW2FSwrCXnHbeEzPmNAlFRUcZeS7KtibON6bixrK7ysyz8ZwY70sIj6/HcuKUEx7m4ym+D45WTAZDj1ZnD51g+CGWDU/kg1HX6qsMja1oAWd1b1mwZPHiwpjlktsoSkKUqzNlZ8ijrdsl6T+YYHnttKVFWHvgeBewlwADIXpK8T7kEjh07Bmn9keDH19e3XNfwJOcRkBYDWR9IVrflQpf616sEOObYHWnpkfFcRcfvVNeWEvrLMIfOIMAAyBlq0UHKEBsbC1kYLDQ01JgV5CDZZjbtLCCDYlesWIHp06dzAKydbatyO5mUINvQFG3hkdlYZuuOjOORLSU4Q6sqyrxWJwEGQDrVhhPnRfaGCgsLMxb6km+NPFxbQLpA5Z8EQZ6enq6NUUOllxla5oBlad2Rf7L6cNEWnpreUqKGaJisiwgwAHKRiq7JYso3ftkbSqa6t2jRoiazwrQ1EpAlEGRF32HDhmmUK+fNirmlhDloWfbTki0lpGXHnKXF7Rect/5ZspsFGADdbMJX7Cggy9gvW7YMQ4cOBdeAsSOsE9xKPpBlfaDevXtz08dqqE8Zr2MGO/IoCxBK66sZ7MiWEpyBWQ3wvKXDCDAAcpiqcryMyhL3sr9Xnz590LlzZ8crAHNc7QIy5kQCZNkqQ8aX8Ki8gLmlhBn0yJYSEuSYXVoyQ4vLD1Tel1c6nwADIOerUy1KJN8+ZWd32dX91ltv1SJPzISeAtHR0YiMjIRsmsrxQOWvI2nRMYMdeZRFCM1gRx65pUT5LXmmawowAHLNeq/WUkvXhnyrDwwMRN++fas1Ld7cOQTCw8Mhs5BGjhzpHAWycylkwLIsMlh0hpZ0X5ndWfIo43l4UIAC5RdgAFR+K55ZDgHZ2HDVqlXG9GYZ98ODAuURkNYLGQ8krYXBwcHlucSpzym6pYS07siWErJuVtEWHllQkgcFKFB5AQZAlbfjlTcIyLdUmeoum5qOHj2a64Xc4MMfyxaQAfPSbTplyhSj++bGs48fP459+/bhxIkTSElJVitK+xotIF27dkVISIgxo+zGaxzlZ3NLCbNLS1p7GjVqZJRPWnfkH1dNd5TaZD4dRYABkKPUVA3kU7okKrLomXRjyFL5EyZM4M7uNVBfzpCkBDl79uwxxgNJF4+sU/Peu2/jqy8+QWZGKvq090LrBhnw8cpFhs0NF1LqIOayB85czjLWFJr7zHMOMeZM/j8xgx15lE2BZZCy2aUlg5c5HsoZfqNZBp0FGADpXDsW5k22qPj+u28RHrYCR48dR3xCMnJVi45P3dro0LYl+g8ahtBpt2H8+PEl/mHesWOH8Qddvr3zD7eFFeeESW3ZssXYcmHnjm2Y/+brmNLDDb/tZ0OXFu6llvZKSj7+szcfn271xJixE/DO+x+V2IpU6g2q+Q1zSwkz6JFJAmawI4/+/v780lDNdcDbU+BGAQZAN4q42M/ybfsvf56LiIh9mNozH4M65KBzgDsa1wNqe7ohNTMfJ+PzsetEHtYf80VsAvDsn/+CPzw2x1g1VrgiIiKMXb5lKjN3fnaxX6BqKG5KSgqmTByDvMTD+MdtNrRs6FbuVLJs+Xg7zB0rD9fDpq07a2TtKWk5lRlaRffQkgIUHb/DLSXKXaU8kQLVJsAAqNpo9b6xjNd57tmn8MVnH+Hp8Tbc3tdDfQP99Q+a6At5mL+uDuIyGmPpinWQsQsSAMnmpt7e3noXmrnTXkAG/8rq0L8ZWFcFP3nl+p0sqVAfbcjBq8tzEBUVhW7dupV0it1ek/+XZFVlc4aWtPLIeB0JeMygx8/Pz27p8UYUoIB9BBgA2cfRoe4i09SnTh6LlLi9eP8eG/zq/nrgc2MB/71LBVDfZWP+/Pl46KGHwD2DbhTizxUVkBmE40YPxbZt23H4tToVvfym89dG5eKlpd7Yve8AWrdufdP7lX1BgjSZlWUGPDJOqX79+oXBjgQ93FKisrq8jgLWCTAAss5ai5Tk22qbwJbo5n8N787Oh0c5Wn1Ky/jGw7l48BMbdu/ebaz2XNp5fJ0C5RF46k9/xOEtX+KDe7IrNPi+rHt/uyMf3x9qjZ17DlR6FpW5pYQ5fkdWr5YtJczWHRnDwy0lyqoFvkcBPQUYAOlZL9WWqzl/eBhLf/gS4c+7V7p7oWjm1hzIxd9W+mJvxCHjA6Hoe3xOgZIEVq48iuefX6O6py5i0aJZmDGjK44ePYqhA0Pw0e/88M32vvhxV1fcOyQCj47ajYAGaSXdptyvPfB5LUx/4G948k9/Ktc1soVE0fE7ssWEzMoyBy1zS4lyMfIkCmgvwABI+yqyXwbXrFmDR++/HSuftKGOV8W7vUrLyYcbgEO2QVi+Ksxu39xLS4uvO4fAunUxarmEz9Xifl7YtWsO/j7vcTRNWoLHRrlBNVJi4v/Oxpo/f2WXwh49n4cHF/gg9tylEtcKkvWHzNYdCXyki7jo+B3ZUkLWtuJBAQo4l4CncxXHtUsj3VsxMQlo376R+kPvoQZmpqrptb4GisxMeXbuHLw4OUsFPx5Vhoq5qBZpa5CKenWy8fsR+Rj75i5s3brV2PW9yjfnDZxeQAbcz5kzAF99tV+t37MA52JXYtN/FxRbYo1WjZJLNLDluqOWh4qQKnAEq+nzrRrmGSuUh4aGFm4pYY7hkWUbzNadXr16cUuJCtjyVAo4sgADIEeuvV/yLsHNHfi8bgAAE+ZJREFUiy+uw/HjCejatSl27DiL1NQsNS6nFd58c7Jx1oYNG5CfGY+Rt1Q9+JEb3v3B7Zg9OBJPjN9pjCN6ZGgm3nh1ngqA1v6SKz5QoGyBDh0a4/PPb1ddYF/Dv/4ENPRZXniBm1t+4XN58t66fmjeIAWRZwIQn+yNt+5ZDU8VCC3cdit+2huMJ8btxNqojlgZ2RHDgmPxzuxVqjWy4BZHzjdBQuIQPDX3Jzz11H71BcEXkvZ994Vg0KBBxhYTxRLjDxSggEsIsF3XCar5m28i8PbbP+OTT2aqQGg0Vqy4D3PnDsG5c0mFpVu44DPM7JlR+HNVnyx/eiEeGb2n8DZTe3tgY/hmyIJvPChQXoHp07uiT0gu4pM64pPw3iVe9vOxQHy+uRdu63sEf7ttI05eboR1hzqowDtfLY54RQVFzbFo9y14fOxOvH/fCqyICMKW6DaF93rpx1GYM2Y7PPLX4K67BuDnn5Mwe/ZIdO8exOCnUIlPKOB6AgyAnKDOT51KVC0+2Th58mphaeSDpVu3ZoU/b9wQhmFBbmp1Zze163bhy5V+0qx+mlooMbfw+rpqTFHPDt5GN1jhi0We2GzXzy3yMp9SAC2aRqCd/3m8tnwIdh5veZNIy4bJeGzMrsLX69XNwvFLjYwWnm6tLhmvT+p5DC0bpWBI57No5JOOU/EFO6OfTfDD3tMt0KVlJmLjLmPs2E5quxYZ41NPrVvlVXhPPqEABVxPgF1gTlDnd93VAy+9tA4jR36M7767y/gjL8WS1iA5ZN+huIuZambNEHRsloil+4MwPPg07h8WgZRML/y0JxiL99yC9+5dgZf/MwKHzjVV7+3HPYMOYP6qQVh9oCPaN03Ev+5fBu/aOdh7qjl+VN+4/dQH0fOhW40uiR/UrJ3oM+2wdOl+NbYjCrNm9cK8eePwyisb0KpVfTXQ9ZwaaJqCr7++U62RUsvIF/9TfgHp5qzIP7lzRc6Xc+WQcWRylPdaObcy18TEXMDZs8nYu3evGqt2Hn+asBd/XTQbf1wwCcue/kZuW3i09U/CA+r3cdn+zriQWA/JGbWRkV3671BdLxuycwq6eut7Z8LdLQ9RZ1rCDQlqvapaaNOmgfGvMAE+oQAFXFKAAZATVLuMZ3jjjUl45pmVaq+uz1QAMlAtUDjZGAgtxYuLi0Ndr+Eq2MnBTNWNcGvrS5jw+izcPShKfdC54VKSLw6cbaY+YILwz1mrselIWzyuPojiEv3w4PB9mNAjBr//dCqW7AvGXQMPGoOft8cEYmhQbKHe/tgAXEtrjMiIODz4YKjayboWvv9+hxqDtAWRkQ+o4MwfY8Z8p7rpNiE0tGOlPjQr80Fb0WvMD34pmPm8vI9yTUXTK881co4csjFtWf/kHHO2UlnnFX2vpq6RRQ/z8/MMY3d3D9Svm4L596zBAx9Pw2NfTIavGlxvHmlZtYzfv4dH7kXoqL3YqH4/y3v41c1WrUe7sWBrT2Rmn8A330ThhRdGFTqV9z48jwIUcD4BBkBOUqdPPTUUvXq1wP33/4j33lMr6R6+hMWLZ6sVausYLUD1feMwuWfB+Jx6dbKQl++O06qbIKh5gtph+zzy17vhvqH71QyxXIztfkKpuGFw5zMIbJxs/OvW6jLOJtQ3tFo2TDEGpJp0/n7pGNjxnBp3EYgGfsfUbtw29UGdo9ZSuYLbbmtubEcg53qqLrMdO46jc+fcMj/IzQ9ouaYiH+hybtFrzeflfaxoenJfK68xEnOS/yQlHVMbnl4yFtAMbN0WV9OOYHrIaTw2ejc+WN8PtwZeLCzp+2oAtBwjupw2Hiv6n+khR1UrkA0nr7TFAw/0QZcuTSt6C55PAQo4oQADICeq1JEjO6jWlidVC8yPaoG5Q6ob6nssW/Y7Y6Cnp8dxNWDUDZ9t6oWsX7oHzG4ENSqomIJMM/byyCn2mnwjT0wvfXuC2rXUHk4eNgQFB0E2RTWPhx9WK/F+f0B1dySp4ET2Rwo0dpQ33+ejawpcuZJudIlK6YO79sLxbQWzB+dO3I49asxOZvb1P01xqttLZnJJS5DMADty3l+NGbqmurnc1e9ywXl5edeHM6Zne8GWW9AFJuc8/uUk3D98NTp3bMyuL9f8dWOpKVCiwPW/GiW+zRcdQWDbtutdUdLi88MP9yAoqAnWro1BSkqW+qPfBufi66ttK6YitFc07ux/8FeL5VbSb4bqLivryFXDRzp27lJ4ikzFHzPmE6MV6tlnh6FpU5/C9/jEdQW++mofXn45DAsW7FOtldswfMQIbD9dEFzLzK53Zq+Ef730QiDpdpXGtvGvzVbT3DvjoRH7jKnvP+66xQjo5cQl+4JwWQVH3+/oisS0ulh3sINqsfRTg/4lSPLAn7+bjchDk+Dj81cVhL9ipF2YAJ9QgAIuKXD9a5ZLFt85Ci0fJqtW3Q8Pj4KoRbpmZHuBV1/dhLS0bGOjUje3aQhptwPSXXU1tfSWnKqI5KgZZj179iy8xSuvbDSeT5wYVPgan1Bg9uzeahr69SnvMh7owjWoLtk8tPV3R1P1O/qmGg9kHgNU9+r2v35sTHuXAEmOR0btKVwQ8elJ281TceeAQ8Y/8wX5XZ/WOxqLI45i3usL0LZtJ1y5kqbGzG1ReehldJma5/KRAhRwLYGSvue7loATlDYnJw//8z8bipVk8+ZT6NevlVrhtp5a2j9XjfnxweajLdWgU6hZXZ2Mc1NVl4J0EWT/0l2Qq8YFySHnyCyadPW+edjUz7lFWoDSsrxUl8T1acSJqe7IUffp06ePeQliY6+pLrmLxqKMx4+r9VrU85SUbGRnF+9eK7yAT1xSwMPDA79/9DH8a9P13zeZYVj08PLMMwIg87Xyrgb9xFeTVMtQKpo0a4B77x2HYcPaYebMbrj77h4MfkxMPlLARQU8XlaHi5bdaYq9bdsZRERcUFPQD0PWBJKWl6ysHCxc+Fs0bFjXaBk6c+YC1m3Ow6ajbY1vxIfVOArpJghqfgXfqNV0T8Y3MmbeBKuF5T7aGILtx1ur6cZ1ENL2PKLONcMXW3qqb+n1IOuu7DzRCj/sVl0NqXXRWV1/TY0N+seyfkjNaqA+VNzRo0eAscZK48beqjsuCh9/vEv9XAsDB7bG++/vMNZgCQm5eb0Xp6kQFqTCAr16heDpl/6JwR1yVCtl2V2tFbn5uau++GTTQPQbMF4Nys9W49EisWZNjNH6I/9v8KAABVxXgJuhOkHdJySkoXFjH2MK9okTV40NJps397upZGPUWIvh/tsxa5C7sSCiDH62xx6PyRn5GP26O7btilAzvDoXS1dae6Rrzuyek9Yo2aeMBwVuFFi48Gu8+OyjWDwnB/W97RMEPfS5G3xbT8W0mc8YXwZGjmyv9vpi4HOjPX+mgCsKMAByoVqPjIzEmBGDsOSJXDWN3T4fMML3zL890bzXXXjvg49cSJNFrQ6B5/7rKaxZ9H/4+uFctehm1X5HX1qch6+3Zqvp9pmoXbt2dWSX96QABRxYgGOAHLjyKpr1Hj164B+v/xOzP/ZU3VsFg0kreo8bz7/voxycyWqDN99698a3+DMFKizw6uvzcUu/Sej5QjYSUiv3OyoLVz6/yB0RCYEMfipcA7yAAq4jwADIderaKOlDDz+MSTPuwYQ38nAlpXIfMCbZu+vy1cDqHGzcvIPfsE0UPlZZYOG3P6gZjK9i8lseWLpPVowu/+/poXN5uP1DL2Q0GIIduyP5e1nl2uANKOC8AuwCc966LbNkr7/2d7z9xiv4cHYOugdWLA7OsuXjq111sSK6KdaEbUazZtc3XS0zUb5JgQoIbNu2DXP/+AiuxcfijpBMjAh2U3vS3fy7Kq2ZW6PzsCjCG0cvumPe3/9Xrfj8AGd5VcCap1LAFQUYALlirf9S5kWLFuEPv38Ao7rk4tHhNrRpcvOHS1GezOx8/LQvD+9v9MLQkePw6edfq9ld3kVP4XMK2F1g8+bN+OLT/1MLe65GhtrYt21AXfiq8UEZ6vcxLiEbKRm5GDKoH+6+92Hccccd8PK6vjyD3TPDG1KAAk4jwADIaaqycgW5du0a3pr/Bj784F20VQHQgLYZ6NwsD0183VBbLcuSkpmPk5fzsfusN7ZFZ2PwoIF44eW/Y8CAAZVLkFdRoAoC8fHxOHnypFpbKtUIvgMCAoyVzs0946pwa15KAQq4mAADIBer8NKKm5OTg/DwcGwK34gjUXvVPk0XYFOv1ffzQ8egrhg4eLixh1fTptxIsjRDvk4BClCAAo4jwADIceqKOaUABShAAQpQwE4CZQ/6sFMivA0FKEABClCAAhTQSYABkE61wbxQgAIUoAAFKGCJAAMgS5iZCAUoQAEKUIACOgkwANKpNpgXClCAAhSgAAUsEWAAZAkzE6EABShAAQpQQCcBBkA61QbzQgEKUIACFKCAJQIMgCxhZiIUoAAFKEABCugkwABIp9pgXihAAQpQgAIUsESAAZAlzEyEAhSgAAUoQAGdBBgA6VQbzAsFKEABClCAApYIMACyhJmJUIACFKAABSigkwADIJ1qg3mhAAUoQAEKUMASAQZAljAzEQpQgAIUoAAFdBJgAKRTbTAvFKAABShAAQpYIsAAyBJmJkIBClCAAhSggE4CDIB0qg3mhQIUoAAFKEABSwQYAFnCzEQoQAEKUIACFNBJgAGQTrXBvFCAAhSgAAUoYIkAAyBLmJkIBShAAQpQgAI6CTAA0qk2mBcKUIACFKAABSwRYABkCTMToQAFKEABClBAJwEGQDrVBvNCAQpQgAIUoIAlAgyALGFmIhSgAAUoQAEK6CTAAEin2mBeKEABClCAAhSwRIABkCXMTIQCFKAABShAAZ0EGADpVBvMCwUoQAEKUIAClggwALKEmYlQgAIUoAAFKKCTAAMgnWqDeaEABShAAQpQwBIBBkCWMDMRClCAAhSgAAV0EmAApFNtMC8UoAAFKEABClgiwADIEmYmQgEKUIACFKCATgIMgHSqDeaFAhSgAAUoQAFLBBgAWcLMRChAAQpQgAIU0EmAAZBOtcG8UIACFKAABShgiQADIEuYmQgFKEABClCAAjoJMADSqTaYFwpQgAIUoAAFLBFgAGQJMxOhAAUoQAEKUEAnAQZAOtUG80IBClCAAhSggCUCDIAsYWYiFKAABShAAQroJMAASKfaYF4oQAEKUIACFLBEgAGQJcxMhAIUoAAFKEABnQQYAOlUG8wLBShAAQpQgAKWCDAAsoSZiVCAAhSgAAUooJMAAyCdaoN5oQAFKEABClDAEgEGQJYwMxEKUIACFKAABXQSYACkU20wLxSgAAUoQAEKWCLAAMgSZiZCAQpQgAIUoIBOAgyAdKoN5oUCFKAABShAAUsEGABZwsxEKEABClCAAhTQSYABkE61wbxQgAIUoAAFKGCJAAMgS5iZCAUoQAEKUIACOgkwANKpNpgXClCAAhSgAAUsEWAAZAkzE6EABShAAQpQQCcBBkA61QbzQgEKUIACFKCAJQIMgCxhZiIUoAAFKEABCugkwABIp9pgXihAAQpQgAIUsESAAZAlzEyEAhSgAAUoQAGdBBgA6VQbzAsFKEABClCAApYIMACyhJmJUIACFKAABSigkwADIJ1qg3mhAAUoQAEKUMASAQZAljAzEQpQgAIUoAAFdBJgAKRTbTAvFKAABShAAQpYIsAAyBJmJkIBClCAAhSggE4CDIB0qg3mhQIUoAAFKEABSwQYAFnCzEQoQAEKUIACFNBJgAGQTrXBvFCAAhSgAAUoYIkAAyBLmJkIBShAAQpQgAI6CTAA0qk2mBcKUIACFKAABSwRYABkCTMToQAFKEABClBAJwEGQDrVBvNCAQpQgAIUoIAlAgyALGFmIhSgAAUoQAEK6CTAAEin2mBeKEABClCAAhSwRIABkCXMTIQCFKAABShAAZ0EGADpVBvMCwUoQAEKUIAClggwALKEmYlQgAIUoAAFKKCTAAMgnWqDeaEABShAAQpQwBIBBkCWMDMRClCAAhSgAAV0EmAApFNtMC8UoAAFKEABClgiwADIEmYmQgEKUIACFKCATgIMgHSqDeaFAhSgAAUoQAFLBBgAWcLMRChAAQpQgAIU0EmAAZBOtcG8UIACFKAABShgiQADIEuYmQgFKEABClCAAjoJMADSqTaYFwpQgAIUoAAFLBFgAGQJMxOhAAUoQAEKUEAnAQZAOtUG80IBClCAAhSggCUCDIAsYWYiFKAABShAAQroJMAASKfaYF4oQAEKUIACFLBEgAGQJcxMhAIUoAAFKEABnQQYAOlUG8wLBShAAQpQgAKWCDAAsoSZiVCAAhSgAAUooJMAAyCdaoN5oQAFKEABClDAEgEGQJYwMxEKUIACFKAABXQSYACkU20wLxSgAAUoQAEKWCLAAMgSZiZCAQpQgAIUoIBOAgyAdKoN5oUCFKAABShAAUsEGABZwsxEKEABClCAAhTQSYABkE61wbxQgAIUoAAFKGCJAAMgS5iZCAUoQAEKUIACOgkwANKpNpgXClCAAhSgAAUsEWAAZAkzE6EABShAAQpQQCcBBkA61QbzQgEKUIACFKCAJQIMgCxhZiIUoAAFKEABCugkwABIp9pgXihAAQpQgAIUsESAAZAlzEyEAhSgAAUoQAGdBBgA6VQbzAsFKEABClCAApYIMACyhJmJUIACFKAABSigkwADIJ1qg3mhAAUoQAEKUMASAQZAljAzEQpQgAIUoAAFdBJgAKRTbTAvFKAABShAAQpYIsAAyBJmJkIBClCAAhSggE4C/x/KfZXM85zwagAAAABJRU5ErkJggg==" /><!-- --></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">$</span>gender))</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>As seen above with the <code>vertex.color</code> argument, you can
specify visual properties as arguments to <code>plot</code> instead of
using vertex or edge attributes. The following plot shows the formal
ties with thick lines while informal ones with thin lines:</p>
<div class="sourceCode" id="cb112"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb112-1"><a href="#cb112-1" tabindex="-1"></a><span class="fu">plot</span>(g,</span>
<span id="cb112-2"><a href="#cb112-2" tabindex="-1"></a>  <span class="at">layout =</span> layout, <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>, <span class="at">vertex.size =</span> <span class="dv">20</span>,</span>
<span id="cb112-3"><a href="#cb112-3" tabindex="-1"></a>  <span class="at">vertex.color =</span> <span class="fu">ifelse</span>(<span class="fu">V</span>(g)<span class="sc">$</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 4.4.2 (2024-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.2
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## 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_2.1.4
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.37     R6_2.5.1          fastmap_1.2.0     Matrix_1.5-4.1   
##  [5] xfun_0.50         lattice_0.22-6    magrittr_2.0.3    glue_1.8.0       
##  [9] cachem_1.1.0      knitr_1.49        pkgconfig_2.0.3   htmltools_0.5.8.1
## [13] rmarkdown_2.29    lifecycle_1.0.4   cli_3.6.3         grid_4.4.2       
## [17] vctrs_0.6.5       sass_0.4.9        jquerylib_0.1.4   compiler_4.4.2   
## [21] rstudioapi_0.17.1 tools_4.4.2       pillar_1.10.1     evaluate_1.0.3   
## [25] bslib_0.8.0       yaml_2.3.10       crayon_1.5.3      rlang_1.1.5      
## [29] jsonlite_1.8.9</code></pre>
</div>



<!-- code folding -->


<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
  (function () {
    var script = document.createElement("script");
    script.type = "text/javascript";
    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
    document.getElementsByTagName("head")[0].appendChild(script);
  })();
</script>

</body>
</html>