0byt3m1n1
Path:
C:
/
Program Files
/
R
/
R-4.2.2
/
library
/
igraph
/
doc
/
[
Home
]
File: igraph.html
<!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"> 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://igraph.org/r/">R</a>, <a href="https://igraph.readthedocs.io/">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://igraph.org/r/html/latest/">https://igraph.org/r/html/latest/</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" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"igraph"</span>)</span></code></pre></div> <p>More details on dependencies, requirements, and troubleshooting on installation are found on the main <a href="https://igraph.org/r/">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" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"igraph"</span>)</span></code></pre></div> <pre><code>## ## Attaching package: 'igraph'</code></pre> <pre><code>## The following objects are masked from 'package:stats': ## ## decompose, spectrum</code></pre> <pre><code>## The following object is masked from 'package:base': ## ## 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" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</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" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</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 “~” character, just like regular formulae in R. The expressions consist of vertex names and edge operators. An edge operator is a sequence of ‘-’ and ‘+’ characters, the former is for the edges and the latter is used for arrow heads. The edges can be arbitrarily long, ie. you may use as many ‘-’ characters to “draw” them as you like. If all edge operators consist of only ‘-’ characters then the graph will be undirected, whereas a single ‘+’ character implies a directed graph: i.e 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" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</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" aria-hidden="true" tabindex="-1"></a>g</span></code></pre></div> <pre><code>## IGRAPH a942b67 UN-- 10 2 -- ## + attr: name (v/c) ## + edges from a942b67 (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" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div> <pre><code>## IGRAPH a942b67 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" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</span> <span class="fu">make_graph</span>(<span class="st">'Zachary'</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" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(g)</span></code></pre></div> <p><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, e.g. 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://igraph.readthedocs.io/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" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</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" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</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" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</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>.</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" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</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> <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>%\>%</code>:</p> <div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</span> g <span class="sc">%>%</span> <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">%>%</span> <span class="fu">add_vertices</span>(<span class="dv">3</span>) <span class="sc">%>%</span></span> <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" 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="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>g</span></code></pre></div> <pre><code>## IGRAPH 0b320dd U--- 40 86 -- Zachary ## + attr: name (g/c) ## + edges from 0b320dd: ## [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="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">get.edge.ids</span>(g, <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">34</span>))</span></code></pre></div> <pre><code>## [1] 82</code></pre> <div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</span> <span class="fu">delete_edges</span>(g, <span class="dv">82</span>)</span></code></pre></div> <p>As an example, to create a broken ring:</p> <div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</span> <span class="fu">make_ring</span>(<span class="dv">10</span>) <span class="sc">%>%</span> <span class="fu">delete_edges</span>(<span class="st">"10|1"</span>)</span> <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(g)</span></code></pre></div> <p><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>"10|1"</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="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</span> <span class="fu">make_ring</span>(<span class="dv">5</span>)</span> <span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</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="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(g)</span></code></pre></div> <p><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/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAJAoAMABAAAAAEAAAJAAAAAAC2FqgIAAEAASURBVHgB7d0JfFTlvf/x70x2EggQIOyrbALKoiARkMUFq3VprVutFou1Wu1tba1X7WLr7fW2aq3aWhX/blWvdStV61J2ZAeRfY0QFlnCDknIPv85c5sUMJOZSeZknnPOZ14vSjJzznN+z/s31a9n9QWCL/FCAAEEEEAAAQQ8JOD30FyZKgIIIIAAAgggEBIgAPFFQAABBBBAAAHPCRCAPNdyJowAAggggAACBCC+AwgggAACCCDgOQECkOdazoQRQAABBBBAgADEdwABBBBAAAEEPCdAAPJcy5kwAggggAACCBCA+A4ggAACCCCAgOcECECeazkTRgABBBBAAAECEN8BBBBAAAEEEPCcAAHIcy1nwggggAACCCBAAOI7gAACCCCAAAKeEyAAea7lTBgBBBBAAAEECEB8BxBAAAEEEEDAcwIEIM+1nAkjgAACCCCAAAGI7wACCCCAAAIIeE6AAOS5ljNhBBBAAAEEECAA8R1AAAEEEEAAAc8JEIA813ImjAACCCCAAAIEIL4DCCCAAAIIIOA5AQKQ51rOhBFAAAEEEECAAMR3AAEEEEAAAQQ8J0AA8lzLmTACCCCAAAIIEID4DiCAAAIIIICA5wQIQJ5rORNGAAEEEEAAAQIQ3wEEEEAAAQQQ8JwAAchzLWfCCCCAAAIIIEAA4juAAAIIIIAAAp4TIAB5ruVMGAEEEEAAAQQIQHwHEEAAAQQQQMBzAgQgz7WcCSOAAAIIIIAAAYjvAAIIIIAAAgh4ToAA5LmWM2EEEEAAAQQQIADxHUAAAQQQQAABzwkQgDzXciaMAAIIIIAAAgQgvgMIIIAAAggg4DkBApDnWs6EEUAAAQQQQIAAxHcAAQQQQAABBDwnQADyXMuZMAIIIIAAAggQgPgOIIAAAggggIDnBAhAnms5E0YAAQQQQAABAhDfAQQQQAABBBDwnAAByHMtZ8IIIIAAAgggQADiO4AAAggggAACnhMgAHmu5UwYAQQQQAABBAhAfAcQQAABBBBAwHMCBCDPtZwJI4AAAggggAABiO8AAggggAACCHhOgADkuZYzYQQQQAABBBAgAPEdQAABBBBAAAHPCRCAPNdyJowAAggggAACBCC+AwgggAACCCDgOQECkOdazoQRQAABBBBAgADEdwABBBBAAAEEPCdAAPJcy5kwAggggAACCBCA+A4ggAACCCCAgOcECECeazkTRgABBBBAAAECEN8BBBBAAAEEEPCcAAHIcy1nwggggAACCCBAAOI7gAACCCCAAAKeEyAAea7lTBgBBBBAAAEECEB8BxBAAAEEEEDAcwIEIM+1nAkjgAACCCCAAAGI7wACCCCAAAIIeE6AAOS5ljNhBBBAAAEEECAA8R1AAAEEEEAAAc8JEIA813ImjAACCCCAAAIEIL4DCCCAAAIIIOA5AQKQ51rOhBFAAAEEEECAAMR3AAEEEEAAAQQ8J0AA8lzLmTACCCCAAAIIEID4DiCAAAIIIICA5wQIQJ5rORNGAAEEEEAAAQIQ3wEEEEAAAQQQ8JwAAchzLWfCCCCAAAIIIEAA4juAAAIIIIAAAp4TIAB5ruVMGAEEEEAAAQQIQHwHEEAAAQQQQMBzAgQgz7WcCSOAAAIIIIAAAYjvAAIIIIAAAgh4ToAA5LmWM2EEEEAAAQQQIADxHUAAAQQQQAABzwkQgDzXciaMAAIIIIAAAgQgvgMIIIAAAggg4DkBApDnWs6EEUAAAQQQQIAAxHcAAQQQQAABBDwnQADyXMuZMAIIIIAAAggQgPgOIIAAAggggIDnBAhAnms5E0YAAQQQQAABAhDfAQQQQAABBBDwnAAByHMtZ8IIIIAAAgggQADiO4AAAggggAACnhMgAHmu5eEnvH37dj3yyCO65MLz1LNrrrKapSklOUmtWmRq2Jl9dfv3JuuDDz5QRUVF+EH4BAEEEEAAAQcI+ALBlwPqpEQbBdauXav777lLn3wyV5cMTlZezzL17eBTm+Y+ZaRKRaXS1n0BLd1SrRmbs7Rtv3T3Pffp+3fcqfT0dBsrY2gEEEAAAQTsESAA2ePqmFEf/PUDevIPD+v2sRW6bqRfqcm+iLVv2lOt309L07ZjrfXeB9PUu3fviOuwAAIIIIAAAiYJEIBM6kYT1lJVVaXrrr5Sm1fM0pRvVygnK3LwObW8v31arR+/Wq7Vq1dr4MCBp37M7wgggAACCBgrQAAytjX2FjZyxDD5jqzT898JKC2KvT7hqvlkY5VueqZCCxYs0MiRI8MtxvsIIIAAAggYJcBJ0Ea1o2mK+dl992jD2pV6cXLjwo9V7ei+SXpucoquvOxi7dixo2kmwFYQQAABBBBopAB7gBoJ6LTV58+fr29ccZE+/FGlWmTEftgr3HyfmxvQokNDNX3WfPl88Rs33PZ4HwEEEHCSwJ49ezR37lytC150Urj3C1WUl6lFy9bq3ad/aO/5mWee6aTpuKJWApAr2hj9JPKGD9bVfdbp8mFJUa1UWp6k9NSqiMtaFxNe/FiqnnjuTV100UURl2cBBBBAwO0C1dXVeuedd/T4I7/R2vUbdE6fdJ2WU6LWzaqVliIdOy4VHMrQ4i1SeSBVN02arP/44Y/Vtm1bt9MYMT8CkBFtaJoilixZomuumKAZP6mIuJdm+4EWenrG2SrY31Kv3f52VAW+u7xK7+88S9NmLYhqeRZCAAEE3CqwfPly3XzjtUou36tbRx/X+NP98vvD7x3fHLy69tXFKXpvRUD33vcL3f3Te9xKY8y8OAfImFbYX8hrf3lRXxsaOfwUl6WouCxVuw9nqawiuj1FVvUTz/BrybLl2rdvn/2TYQsIIICAoQKvv/66LpowRjcNLtCbt5Xr/IFJ9YYfaxq92/v1wOVVmnpHpd56/r917TeukLUHiZd9AgQg+2yNG3nm9I80pk/k+15mplWof8f96tTqWHBPUfTTsO4hNLJvhubMmRP9SiyJAAIIuEjg5Zde0C03f0svTa7QFVGeanDi9Lvk+PXK5DL99a2/q1OHXJWXl5/4MT/HUYAAFEdMk4ey7vuzIX976A7PdtbZO6dYq1attHMTjI0AAggYKfDxxx/rpm/frPd+5A/+R2TD//WanOTTlt+n64yOJbrphmuMnKsbimp4h9wwew/NobCwUDnZ6Y265080XJ1bBbSzID+aRVkGAQQQcI1AQUGBbrjuKv39R6nq1iY+/2r94zerlL9yhn7/6COucTJpIvHpkkkzopY6BUpKSpSRFv35PHUOEsWbzVJ9KikpimJJFkEAAQTcI3Bf8HmK3zynSoO6xO9fq9aeoIevKtNv/usB7d8ffAgjr7gKxK9TcS2LweItkJmZqZLSyngP+6XxisoCymqe/aX3eQMBBBBwq8DWrVv1z48/0q3nxX7S8hcHm9fLYu1N+soZ0pNP/KHe5fgwdgECUOxmjlwjNzdXR4rLg1d3RT4JujET3HYgST1O69+YIVgXAQQQcJTAyy++oMuH+oL3TIt81cjTM85Sz7t+WPvn0Q/zIs71uuEVevmFKRGXY4HYBJJjW5ylnSpg3Z15yBn9tXL7BuX1tu9Q2Jo9GcErH4Y5lYm6EUAAgZgF3v/7G/rxudYe9vr3KVg3lt20J0cf3v2X2m10DF5tG+llnVAdqCzR2uBdpAcMGBBpcT6PUqD+bkU5CIs5Q+CCiZdp9sboM+/ximRVVUf/FTlWGtCqrcc1evRoZ4BQJQIIINBIgYqKCq3Z8LmG9oi89+d/Fw1SZfCfqQX7WgZvM3I0eFXuATVPj+4y9+E9/Vq8eHEjq2X1EwWi/7fbiWvxsyMFbvjWTfr78uAt1yvrPwxWVpmkt5f21ycbumntzrb627J+OlycFnHO7ywNPg7j4gtlnW/ECwEEEPCCgHX1V4ecZkoJnrAc6fXhyt56/7O+uu3Fr+qC396k6Wt7RFql9vNuLUu0edPG2t/5ofEC0e8OaPy2GCHBAr1791beqNF6ef4cTT4vfDFpyVX6+tnrQ3/CL3XyJ2UVAT0zN1n/+OcDJ3/AbwgggICLBY4eParsZtGdVvDGnW+G7rA/c10PPfTuaN3+wqV6/yevqk/7gxGFmqdLe49EXi7iQCxQK8AeoFoKb/zw0O/+oGfmJGnHgdivVqhP6LcfJmv8BRdr6NCh9S3GZwgggICrBNLS0nS8PPIDo2sm3aFlkb6Zt1p/++HratmsVM/OPKvmo3r/Lq2Q0tKCKYhX3AQIQHGjdMZA/fv312NP/Fnfei5ZB4vqPxQW7Yx+8r+VWryrrZ6Z8mK0q7AcAggg4AoB68ntB44G00mMr97BvT4Xn5mvDbvbRLXmoeNJate+U1TLslB0AgSg6JxctdS3vnWjrr5+siY+Uh3cpdq4EPTSJ1V6Z2ml5i9arqysLFc5MRkEEEAgkoB1i5HKKp8ONOA/KId036XsjLJImwh9vnl/M/Xr1y+qZVkoOgECUHROrlvq4d8/rvt++Rtd9rhPq3bEfjissiqghz9K0msrc2WdBNiqVSvXGTEhBBBAIBqB88bkad7G2P85umJbBw3rsSviJqwLV5bml2jMmDERl2WB6AUIQNFbuW7JH911t6a8+Lpu/UuG7n07SdujOC+oIhh83l1epYmPpWhncp6WLl+jbt26uc6GCSGAAALRClzzzZv17upm9S6+pbCV3lpyevAq3P/71651T6C1O9vp1vHL6l3P+nDammqdPWyIWrduHXFZFohewBcIvqJfnCXdKHDkyJHgw/Ye1lN/ekI92yVpZPfj6pNbrbYtfMpIkaz7+xTsC2jZjgzN3VCpgQMH6f7g3qMJEybIusEiLwQQQMDLAuXl5erRtYOm3FCk/p3q3q9g3VLkG09eHTrx+eoRa5Xkr5b1d252cUS6rz6Rov958jVdeumlEZdlgegFCEDRW7l+ycrKSs2aNUuzZ83QulXLtHPnzuCDTUuU06aNevfprxF552nixInq2rWr6y2YIAIIIBCLwJQpz+rZh3+iN24Lf0J0cVlK8FyhDFlXgqUkRXfI7PVFVXpv6+lasGRFLOWwbBQCBKAokLy6yJYtW5Sfn68LL7zQqwTMGwEEEIhKoKqqShdOGK0eSSt0/6XxObCyKL9Kd7yaornzl/AIjKi6ENtCde+ri20MlnaxAIe4XNxcpoYAAnETSEoK3kF/6gea9XkrPT0z+vsChSsgf2+1rn+qQq+/OZXwEw6pke9zJ+hGArp5dev0MAKQmzvM3BBAIJ4CLVu21PKV62T9vbkwSY9c07B/hn5WUK3vvhQMVG+9qQsuuCCeJTLWCQLsAToBgx9PFuD8+JM9+A0BBBCIJJCdnS3rwpKizLN01VMpWrMzunN9rHGPHg/odx/69b1X0/XOux/pa1+/KtLm+LwRAgSgRuB5YVX2AHmhy8wRAQTiKdCiRQt9NH2Ovn/vY7rttea68fm04A1jq7Tn8JfPDSotDwSvrq3Sz/+WpHG/9au805VasWo99/yJZ0PCjMVJ0GFgeFvavHmzduzYofHjx8OBAAIIINAAAevq2qlTp+r1V57XnE/mqbysXG2y05SW6texkkodOFKmYYNP16VXXKMbgnfp79SJx100gLlBqxCAGsTmjZU2bdoUuhSeAOSNfjNLBBCwX+Dw4cPau3evKioqZO0p6tKlC+da2s9e5xY4CbpOFt6sEeAQWI0EfyOAAAKNF7BOkLb+8Eq8AOcAJb4HxlbAVWDGtobCEEAAAQQaKUAAaiSgm1fnKjA3d5e5IYAAAt4WIAB5u/8RZ88hsIhELIAAAggg4EABApADm9ZUJXMIrKmk2Q4CCCCAQFMLEICaWtxB2+MQmIOaRakIIIAAAjEJEIBi4vLewhwC817PmTECCCDgBQECkBe63MA5cgisgXCshgACCCBgvAAByPgWJa5ADoElzp4tI4AAAgjYK0AAstfX8aNzCMzxLWQCCCCAAAJ1CBCA6kDhrf8T4BAY3wQEEEAAAbcKEIDc2tk4zItDYHFAZAgEEEAAASMFCEBGtsWcojgEZk4vqAQBBBBAIH4CBKD4WbpuJA6Bua6lTAgBBBBA4F8CBCC+CmEFOAQWloYPEEAAAQQcLkAAcngD7S6fQ2B2CzM+AggggEAiBAhAiVB3yDY5BOaQRlEmAggggEDMAgSgmMm8swKHwLzTa2aKAAIIeE2AAOS1jsc4Xw6BxQjG4ggggAACjhAgADmiTYkpkkNgiXFnqwgggAAC9gsQgOw3duwWCECObR2FI4AAAghEECAARQDiYwQQQAABBBBwnwAByH09jduM2AMUN0oGQgABBBAwTIAAZFhDTCqHAGRSN6gFAQQQQCCeAgSgeGoyFgIIIIAAAgg4QoAA5Ig2JaZI9gAlxp2tIoAAAgjYL0AAst/YsVsgADm2dRSOAAIIIBBBgAAUAYiPEUAAAQQQQMB9AgQg9/U0bjNiD1DcKBkIAQQQQMAwAQKQYQ0xqRwCkEndoBYEEEAAgXgKEIDiqclYCCCAAAIIIOAIAQKQI9qUmCLZA5QYd7aKAAIIIGC/AAHIfmPHboEA5NjWUTgCCCCAQAQBAlAEID5GAAEEEEAAAfcJEIDc19O4zYg9QHGjZCAEEEAAAcMECECGNcSkcghAJnWDWhBAAAEE4ilAAIqnpsvGIgC5rKFMBwEEEECgVoAAVEvBDwgggAACCCDgFQECkFc63YB5sgeoAWisggACCCDgCAECkCPalJgiCUCJcWerCCCAAAL2CxCA7DdmCwgggAACCCBgmAAByLCGmFQOe4BM6ga1IIAAAgjEU4AAFE9Nl41FAHJZQ5kOAggggECtAAGoloIfEEAAAQQQQMArAgQgr3S6AfNkD1AD0FgFAQQQQMARAgQgR7QpMUUSgBLjzlYRQAABBOwXIADZb8wWEEAAAQQQQMAwAQKQYQ0xqRz2AJnUDWpBAAEEEIinAAEonpouG4sA5LKGMh0EEEAAgVoBAlAtBT8ggAACCCCAgFcECEBe6XQD5skeoAagsQoCCCCAgCMECECOaFNiiiQAJcadrSKAAAII2C9AALLf2LFbsAIQLwQQQAABBNwoQAByY1fjOCefzxfH0RgKAQQQQAABMwQIQGb0wcgqOARmZFsoCgEEEEAgDgIEoDggunUIDoG5tbPMCwEEEECAAMR3oF4BDoHVy8OHCCCAAAIOFSAAObRxTVE2h8CaQpltIIAAAggkQoAAlAh1h2yTQ2AOaRRlIoAAAgjELEAAipnMWytwCMxb/Wa2CCCAgFcECEBe6XQD5skhsAagsQoCCCCAgCMECECOaFNiiuQQWGLc2SoCCCCAgP0CBCD7jR29BQ6BObp9FI8AAgggEEaAABQGhrclDoHxLUAAAQQQcKsAAcitnY3DvDgEFgdEhkAAAQQQMFKAAGRkW8wpikNg5vSCShBAAAEE4idAAIqfpetG4hCY61rKhBBAAAEE/iVAAOKrEFaAQ2BhafgAAQQQQMDhAgQghzfQ7vI5BGa3MOMjgAACCCRCgACUCHWHbJNDYA5pFGUigAACCMQsQACKmcw7K3AIzDu9ZqYIIICA1wQIQF7reIzz5RBYjGAsjgACCCDgCAECkCPalJgiOQSWGHe2igACCCBgvwAByH5jx26BQ2CObR2FI4AAAghEECAARQDy+sccAvP6N4D5I4AAAu4UIAC5s69xmRWHwOLCyCAIIIAAAgYKEIAMbIopJXEIzJROUAcCCCCAQLwFCEDxFnXZeBwCc1lDmQ4CCCCAQEiAAMQXIawAh8DC0vABAggggIDDBQhADm+gneVzCMxOXcZGAAEEEEikAAEokfoO2DaHwBzQJEpEAAEEEIhZgAAUM5l3VuAQmHd6zUwRQAABrwkQgLzW8RjmyyGwGLBYFAEEEEDAUQIEIEe1q+mL5RBY05uzRQQQQAAB+wUIQPYbO3YLHAJzbOsoHAEEEEAgggABKAKQlz/mEJiXu8/cEUAAAXcLEIDc3d9Gz45DYI0mZAAEEEAAAQMFCEAGNsWUkjgEZkonqAMBBBBAIN4CBKB4i7poPA6BuaiZTAUBBBBA4CQBAtBJHPxyqgCHwE4V4XcEEEAAATcIEIDc0EWb5sAhMJtgGRYBBBBAIOECBKCEt8DcAjgEZm5vqAwBBBBAoHECBKDG+bl+bQ6Bub7FTBABBBDwpAAByJNtj27SHAKLzomlEEAAAQScJ0AAcl7PmqxiDoE1GTUbQgABBBBoYgECUBODO21zfj9fEaf1jHoRQAABBCIL8G+3yEaeXIK9P55sO5NGAAEEPCNAAPJMq5koAggggAACCNQIEIBqJPj7JAFOgD6Jg18QQAABBFwmQAByWUPjNR0CULwkGQcBBBBAwEQBApCJXTGgJs4BMqAJlIAAAgggYJsAAcg2WucPzE0Qnd9DZoAAAgggULcAAahuF8+/yyEwz38FAEAAAQRcLUAAcnV7Gz45DoE13I41EUAAAQTMFyAAmd+jhFXIIbCE0bNhBBBAAAGbBQhANgM7dXgOgTm1c9SNAAIIIBCNAAEoGiUPLsMhMA82nSkjgAACHhIgAHmo2bFOlUNgsYqxPAIIIICAUwQIQE7pVBPXySGwJgZncwgggAACTSpAAGpSbudsjENgzukVlSKAAAIIxC5AAIrdzDNrcAjMM61moggggIDnBAhAnmt5dBPmEFh0TiyFAAIIIOBMAQKQM/tme9UcArOdmA0ggAACCCRQgACUQHzTN80hMNM7RH0IIIAAAg0VIAA1VM7l63EIzOUNZnoIIICAxwUIQB7/AoSbPofAwsnwPgIIIICAGwQIQG7ook1z4BCYTbAMiwACCCCQcAECUMJbYGYBHAIzsy9UhQACCCAQHwECUHwcXTcKh8Bc11ImhAACCCBwggAB6AQMfjxZgENgJ3vwGwIIIICAewQIQO7pZVxnwiGwuHIyGAIIIICAYQIEIMMaYko5HAIzpRPUgQACCCBghwAByA5Vl4zJITCXNJJpIIAAAgh8SYAA9CUS3rAEOATG9wABBBBAwM0CBCA3d7cRc+MQWCPwWBUBBBBAwHgBApDxLUpcgRwCS5w9W0YAAQQQsFcg2d7hGd1JAqWlpVq8eLE2btyoLVu2BP98rq1bt6pXr14655xz1KFDBydNh1oRQAABBBAIK+ALHuoIhP2UDzwhMH/+fP3hkYf08bTp6telmXq3rVSr9ONKC8bjovIUFRxK1+JNJeretbMm3XKHJt9yizIzMz1hwyQRQAABBNwpQAByZ1+jmpW1d+f2Wydp/epl+u7oMl06JEnN0311rmvl5CWfB/Ta0jQt2erXQ797TJMmTapzWd5EAAEEEEDAdAECkOkdsqm+2bNn65qrLtekvFLdcp5Pfn/dwaeuza/7olp3v5WmcRdfoz8+9aw4V6guJd5DAAEEEDBZgABkcndsqs0KP1+ZeIGemeTTqD5JDdpKWUVA/e8p07Ahg7R46WdKSmrYOA3aOCshgAACCCDQSAGuAmskoNNWX7ZsmcaNG6fXbvM3OPxYc05L8Sn/kTSlluTr9u9NdhoD9SKAAAIIeFyAAOShL8C+fft0+aUX6flbUnRm18a33jps9vx3qrVg+tt6+uk/e0iSqSKAAAIIOF2AQ2BO72AM9d9x+3dVvP41/eKyqhjWirzo5j3VuuG5NG3M36ZWrVpFXoElEEAAAQQQSLBA43cDJHgCbD46gcLCQr36yiu6c0JlVCscLEpXRVV0X4/e7f0a1y+gp/70ZFRjsxACCCCAAAKJFoju33CJrpLtN1rglb/8RecPTFKrzPqv9lqxrb2uf+rryvv1ZI18YLIenDpGx8sj3y/zmyPK9dwzTzW6TgZAAAEEEECgKQQIQE2hbMA23v3b/+qSgWX1VnL0eJqemz1UP/nKAi1/8GndccESvTB3qP44bXi961kfDuriV6CyROvWrYu4LAsggAACCCCQaAECUKI70ATbt25iuGz5ap3ds/52r9yeq199fZaGdt+tZmmV+vaYFerZ7qBW78iNqsoRvXxasGBBVMuyEAIIIIAAAokUiHxsI5HVse24COzcuVPZWSnBUFP/yc+j+27/0vZ6tD2k9tlFX3q/rje6tyzR5k0b6vqI9xBAAAEEEDBKoP5dAkaVSjENFThy5IiyM1NiXr26Wlr/RVtdn7c6qnWzm0mHD+6PalkWQgABBBBAIJECBKBE6jfRtpOTk1VeEUwzMb7e/ayvLj5zs/p3jC7UlAcvMEsKbosXAggggAACpgsQgEzvUBzqy8nJ0eGiiphGOlCUoZnreuqeS+dFvd7h4361adch6uVZEAEEEEAAgUQJEIASJd+E223btq2qqn06UBSIaqvWoa/HPhypX145W8lJ0a1jDZx/oJn69esf1TZYCAEEEEAAgUQKEIASqd+E2x4zeqQWbIruMNjTM8/Wzed9ppys47UVbj/QovbncD8s3FiqsWPHhvuY9xFAAAEEEDBGgABkTCvsLeSab96sd9cEz1KO8Hp94UAdKk7XlsJWmr6mpz5e1UsP/yNP2w9k17vmnPVV6n1aL3Xs2LHe5fgQAQQQQAABEwR4FpgJXWiCGkpLS9W1c65evrlUfTvUnXsXbu6sG5/5WvBw2cmf5wYvg5//8+fkP/ntk6q+/tkUff/+p3TDDTec9D6/IIAAAgggYKIAAcjErthU05NPPK43nvm5Xp4c2wnRkcr55+oqPT6vk1avz1dSUlKkxfkcAQQQQACBhAvU89/0Ca+NAuIs8L3bbldVZi/9aUb8Bt66r1q/mJqiKS+8QviJHysjIYAAAgjYLMBNW2wGNmn4lJQUTX3/Yw0bPECtmx3WdSMb1/7CowFNeCj4ENQpf9K5555r0lSpBQEEEEAAgXoFGvdvwHqH5kMTBdq1a6dln61R+/bttXGPXw9c2bCdgNaenxunJOuJxx/VdyZPNnGq1IQAAggggEBYgYb92y/scHzgBIHc3FwVFhZqQ3FffeeFZO04EN3l8dbcKqsCemleQN94Kkn3/Pwh3fmDu5wwZWpEAAEEEEDgJAEC0Ekc3vnFujniJwuX6aLr79XX/pSqH/01RbODl7KXVdR948P1u6r1xLSAzvttkhYfO0fTZs5Tq9atZT1olRcCCCCAAAJOE+AqMKd1zIZ6jx07ppdffll/ffV5ffrZanXIyVCb5n6lpvh1rDSgz3cVq12b1rr0q1dq0uRbdeaZZ4aq2LVrl2bOnKmrrrpK6enpNlTGkAgggAACCNgjQACyx9Wxo1ZUVOjzzz8PHSKzfm7evLl69+6tVq1a1TmnJUuW6ODBg5o4cWKdn/MmAggggAACJgoQgEzsioNqqg4+OGzq1Knq06ePBg4c6KDKKRUBBBBAwMsCnAPk5e7HYe7+4O2hzz//fH366aehPUFxGJIhEEAAAQQQsF2AAGQ7sfs30KJFC40cOVLTp09XZWWl+yfMDBFAAAEEHC9AAHJ8C82YgHUIrE2bNlq4cKEZBVEFAggggAAC9QgQgOrB4aPYBEaNGhW6LL6goCC2FVkaAQQQQACBJhYgADUxuJs3l5qaqgkTJmju3LkqLi5281SZGwIIIICAwwUIQA5voGnlW4/aGDRoUOj+QIFA3TdVNK1m6kEAAQQQ8J4AAch7Pbd9xoMHDw5tY8WKFbZviw0ggAACCCDQEAECUEPUWKdeAZ/Pp/Hjx2vNmjXau3dvvcvyIQIIIIAAAokQIAAlQt0D28zMzNTo0aNDh8LKy8s9MGOmiAACCCDgJAECkJO65bBau3fvri5dumjevHkOq5xyEUAAAQTcLkAAcnuHEzy/c845R/v379emTZsSXAmbRwABBBBA4N8CBKB/W/CTDQLJycmhR2VYN0g8evSoDVtgSAQQQAABBGIXIADFbsYaMQq0bt1aw4YN04wZM2Q9PJUXAggggAACiRYgACW6Ax7ZvvWk+IyMDC1dutQjM2aaCCCAAAImCxCATO6Oy2obO3asNm/erC+++MJlM2M6CCCAAAJOEyAAOa1jDq43PT1d48aN06xZs1RaWurgmVA6AggggIDTBQhATu+gw+rv1KmTevfurdmzZzuscspFAAEEEHCTAAHITd10yFzOPvtslZSUaO3atQ6pmDIRQAABBNwmQAByW0cdMB+/3x+6NH7ZsmU6ePCgAyqmRAQQQAABtwkQgNzWUYfMp0WLFho5cmTo0vjKykqHVE2ZCCCAAAJuESAAuaWTDpxHnz59lJOTo0WLFjmwekpGAAEEEHCyAAHIyd1zQe2jRo3Sjh07VFBQ4ILZMAUEEEAAAacIEICc0imX1pmamqrx48frk08+UXFxsUtnybQQQAABBEwTIACZ1hEP1pObm6sBAwaE7g8UCAQ8KMCUEUAAAQSaWoAA1NTibK9OgSFDhsgKPytWrKjzc95EAAEEEEAgngIEoHhqMlaDBXw+X+hQ2OrVq1VYWNjgcVgRAQQQQACBaAQIQNEosUyTCGRmZmrMmDGaPn26ysvLm2SbbAQBBBBAwJsCBCBv9t3YWXfv3l1dunTRvHnzjK2RwhBAAAEEnC9AAHJ+D103A+sGifv37w89Od51k2NCCCCAAAJGCBCAjGgDRZwokJycHHpUxsKFC3X06NETP+JnBBBAAAEE4iJAAIoLI4PEW6B169YaOnRo6FEZ1dXV8R6e8RBAAAEEPC5AAPL4F8Dk6Q8cOFDp6emyHprKCwEEEEAAgXgKEIDiqclYcRcYN26cNm3apF27dsV9bAZEAAEEEPCuAAHIu713xMytPUBjx47VzJkzVVpa6oiaKRIBBBBAwHwBApD5PfJ8hZ07d9Zpp52mOXPmeN4CAAQQQACB+AgQgOLjyCg2CwwfPjz0sNS1a9favCWGRwABBBDwggAByAtddsEc/X6/JkyYEDoh+uDBgy6YEVNAAAEEEEikAAEokfpsOyaB7OxsWTdJnDFjhiorK2Nal4URQAABBBA4UYAAdKIGPxsv0KdPH1n3CFq0aJHxtVIgAggggIC5AgQgc3tDZWEERo8ere3bt6ugoCDMEryNAAIIIIBA/QIEoPp9+NRAgdTU1ND5QHPnzg2dGG1giZSEAAIIIGC4AAHI8AZRXt0Cubm5su4UPWvWLAUCgboX4l0EEEAAAQTCCBCAwsDwtvkCQ4YMkfWcsJUrV5pfLBUigAACCBglQAAyqh0UE4uAz+fT+PHjtWrVKhUWFsayKssigAACCHhcgADk8S+A06eflZUl66Ro69L48vJyp0+H+hFAAAEEmkiAANRE0GzGPoEePXqoU6dOmj9/vn0bYWQEEEAAAVcJEIBc1U7vTiYvLy90GCw/P9+7CMwcAQQQQCBqAQJQ1FQsaLJAcnKyzj///NBeoKNHj5pcKrUhgAACCBggQAAyoAmUEB+BnJwcDRs2LHQ+kHV1GC8EEEAAAQTCCRCAwsnwviMFrHsDpaenhx6a6sgJUDQCCCCAQJMIEICahJmNNKXA2LFjtWnTJu3ataspN8u2EEAAAQQcJEAAclCzKDU6gYyMDFkhyLpLdGlpaXQrsRQCCCCAgKcECECeard3Jtu5c2f16tVLc+bM8c6kmSkCCCCAQNQCBKCoqVjQaQLDhw9XUVGR1q1b57TSqRcBBBBAwGYBApDNwAyfOAG/3x+6NH7p0qU6dOhQ4gphywgggAACxgkQgIxrCQXFUyA7O1vnnHNO6NL4ysrKeA7NWAgggAACDhYgADm4eZQenUDfvn3VsmVLLV68OLoVWAoBBBBAwPUCBCDXt5gJWgJjxozRtm3bQn8QQQABBBBAgADEd8ATAqmpqZowYULoqrCSkhJPzJlJIoAAAgiEFyAAhbfhE5cJ5ObmasCAAZo5c6YCgYDLZsd0EEAAAQRiESAAxaLFso4XGDp0qKznhK1cudLxc2ECCCCAAAINFyAANdyONR0o4PP5NH78eK1atUr79u1z4AwoGQEEEEAgHgIEoHgoMoajBLKysjR69GhNnz5dFRUVjqqdYhFAAAEE4iNAAIqPI6M4TKBHjx7q1KmT5s2b57DKKRcBBBBAIB4CBKB4KDKGIwXy8vJUWFio/Px8R9ZP0QgggAACDRcgADXcjjUdLpCcnBx6VMaCBQt09OhRh8+G8hFAAAEEYhEgAMWixbKuE8jJydGQIUNCl8ZbV4fxQgABBBDwhgAByBt9Zpb1CAwaNEjWjRI//fTTepbiIwQQQAABNwkQgNzUTebSYIFx48Zpw4YN2rVrV4PHYEUEEEAAAecIEICc0ysqtVEgIyNDVgiaNWuWSktLbdwSQyOAAAIImCBAADKhC9RghEDnzp3Vs2dPzZ0714h6KAIBBBBAwD4BApB9tozsQIHhw4fr2LFjWrdunQOrp2QEEEAAgWgFCEDRSrGcJwSSkpJCT41ftmyZDh065Ik5M0kEEEDAiwIEIC92nTnXK9CyZUtZe4JmzJihqqqqepflQwQQQAABZwoQgJzZN6q2WaBfv36ygtDixYtt3hLDI4AAAggkQoAAlAh1tukIAeuBqQUFBdq+fbsj6qVIBBBAAIHoBQhA0VuxpMcE0tLSNH78eM2ZM0clJSUemz3TRQABBNwtQAByd3+ZXSMF2rdvr9NPPz10f6BAINDI0VgdAQQQQMAUAQKQKZ2gDmMFrGeFWSdDr1q1ytgaKQwBBBBAIDYBAlBsXiztQQG/3x86FLZy5Urt27fPgwJMGQEEEHCfAAHIfT1lRjYIZGVladSoUaFL4ysqKmzYAkMigAACCDSlAAGoKbXZlqMFrMdkdOjQQfPnz3f0PCgeAQQQQEAiAPEtQCAGgXPPPVd79+5Vfn5+DGuxKAIIIICAaQIEINM6Qj1GCyQnJ4celbFgwYLQM8OMLpbiEEAAAQTCChCAwtLwAQJ1C7Rp00aDBw8OnQ9UXV1d90K8iwACCCBgtAAByOj2UJypAoMGDVJqaqqWL19uaonUhQACCCBQjwABqB4cPkIgnIDP59O4ceO0fv167d69O9xivI8AAgggYKgAAcjQxlCW+QIZGRkaO3asZs6cqbKyMvMLpkIEEEAAgVoBAlAtBT8gELtAly5dZF0ebz0vjBcCCCCAgHMECEDO6RWVGiowfPjw0BVh1uEwXggggAACzhAgADmjT1RpsEBSUlLo0vglS5bo8OHDBldKaQgggAACNQIEoBoJ/kagEQItW7bUiBEjNH369NCDUxsxFKsigAACCDSBAAGoCZDZhDcE+vXrp+zsbC1evNgbE2aWCCCAgIMFCEAObh6lmycwZswYFRQUaPv27eYVR0UIIIAAArUCBKBaCn5AoPECaWlpGj9+fOiqsJKSksYPyAgIIIAAArYIEIBsYWVQLwu0b99e/fv316xZsxQIBLxMwdwRQAABYwUIQMa2hsKcLDB06FBVVlZq9erVTp4GtSOAAAKuFSAAuba1TCyRAn6/P3Rp/IoVK7R///5ElsK2EUAAAQTqECAA1YHCWwjEQyArK0ujRo0KXRpfUVERjyEZAwEEEEAgTgIEoDhBMgwCdQlYj8no0KGD5s+fX9fHvIcAAgggkCABAlCC4NmsdwTy8vK0Z88eff75596ZNDNFAAEEDBcgABneIMpzvkBKSorOP//80F6gY8eOOX9CzAABBBBwgQAByAVNZArmC7Rp00aDBw/WzJkzVV1dbX7BVIgAAgi4XIAA5PIGMz1zBAYNGiRrb9Dy5cvNKYpKEEAAAY8KEIA82nim3fQCPp9PY8eO1fr167V79+6mL4AtIoAAAgjUChCAain4AQH7BZo1axYKQdahsLKyMvs3yBYQQAABBOoUIADVycKbCNgn0KVLF/Xo0UNz5861byOMjAACCCBQrwABqF4ePkTAHoERI0boyJEj2rBhgz0bYFQEEEAAgXoFCED18vAhAvYIJCUlhS6NX7JkiQ4fPlzvRqxL5/fu3Ssuoa+XiQ8RQACBmAR8wadV87jqmMhYGIH4CVgnRK9bt05XXHGFrFBkvTZt2qTXXv2LZnz8nlau2ahA8LL5zIxklZRWBpdJ1pln9NeFF1+h6795g7p37x5ah/9BAAEEEIhNgAAUmxdLIxB3gWnTpikzM1MtWrTQvT/9oZYsWqjLh0pjeldoQCe/Wmb6ard5oCigtTurNWtTqt5bXq1x48frv3/7e/Xp06d2GX5AAAEEEIgsQACKbMQSCNgqUF5erv/4wR164/VXdOf4Kl2f51NK0r9DT7iNl1UE9FLwEWNT5ibp948/pRtvvCncoryPAAIIIHCKAAHoFBB+RaCpBW69ZZJmffim/nJLpdq1iBx8Tq1vx4Fqnfebcv3n3Xfpod89eurH/I4AAgggUIdAch3v8RYCCDSRwPdv+66mv/+apv7Ap6z02MOPVWaXHL8+fTBNlz72R6Wmp+tXv/5NE1XPZhBAAAHnCnAVmHN7R+UOF3huyhS99sqLeufOhoefGoJWwfOEpv6HT8/9+Q966623at7mbwQQQACBMAIcAgsDw9sI2CmwY8cODR7UT+/cUamuwT048Xqt31Wtbz+fprUbPlfbtm3jNSzjIIAAAq4TiN8/eV1Hw4QQsE/ggV/cqxtGVkcVfo6XJ2vXoayoiunf0a9LzqjSw7/976iWZyEEEEDAqwLsAfJq55l3wgQOHTqk7l07au69AbXICH/eT2WVTz9/a4J2HGyhVdtzdVr7g/rzt99XbnZxvbXvORzQV/6QpF179isjI6PeZfkQAQQQ8KoAe4C82nnmnTCBqVOn6rz+afWGH6u4GWt76s4LF+mV297R4l9N0RfBIPTSJ4Mj1t2+pU+Du6fpo48+irgsCyCAAAJeFSAAebXzzDthAjOn/UPn9qx/L45V3MjeO9SxVVGozozUSp3RZa+6t63/sRk1k8rrfkwzpxOAajz4GwEEEDhVgAB0qgi/I2CzwNo1q9S3Q+T/67XIKK+tZEthKzVLK9eVZ62vfa++H/p28Gnt6s/qW4TPEEAAAU8LRP6nsKd5mDwC8RfYs3df1Dc8LK/065mZw3T5Y9dp854cFexrGVVBbZv7tHfP3qiWZSEEEEDAiwIEIC92nTknVKC0rELNUqMroarar2E9dun75y+RtRfo9hcvUVV1+BOna0ZtliaVlpXV/MrfCCCAAAKnCHAn6FNA+BUBuwWaZ2aoqPSYsptF3pJ17s9ZPXaH/uRmF+nHr03U1n2tdFruwXpXLiqVmmdl1rsMHyKAAAJeFmAPkJe7z9wTItCzRzdt3V8d87YnDNgqny+g6kDkVQv2BdSzZ6/IC7IEAggg4FEBApBHG8+0EycwfOR5WrY18mGsUyu0bojYslmperU7dOpHX/r90+3J6jtgqKqrYw9aXxqMNxBAAAEXChCAXNhUpmS2wMWXfFWzN0e+QeE/V/fSvmP/Pk72wcre+vkVs5Xkj7wLaOaGJLVt104vv/yyZsyYofz8fJWX//uqMrOFqA4BBBCwX4A7QdtvzBYQOEnA2ivTo2t7PXn1EQ3qEv6/QSY9e7kWbu6irwzeHFxub+i8n9F9t580Vl2/zNsUfBTGnK5atS5fJSUl2rZtW+jP7t27Q88H69atm7p3767mzZvXtTrvIYAAAp4QIAB5os1M0jSBP/3pj3rrmfv1/KT698ocK01VekqlUpKiO5QVCAT0jT+n6e4Hn9G111570rQrKyu1c+fO2kBkPSajJgxZD071+WI/LHfSBvgFAQQQcJAAAchBzaJU9wiUBS9RP2vIAN00eIe+fnb4vUCxzviFT6TpX/TTJwuWKSkpKezqVlAqLCysDUPHjx8PhSErEHXu3FnJyVwgGhaPDxBAwBUCBCBXtJFJOFFgy5YtGnHWYD16dalG9w0fVqKd27vLq3TPG9KWrQXq2LFjtKuFljt69GhtGNq3b586dOhQG4iaNfv3eUgxDcrCCCCAgMECBCCDm0Np7heYP3++Ro0apf+6KlnX5zV8r4sVfn74SoV27NgR2oPTGDlr75Q1jnXukPV3dnZ27aGy1q1bN2Zo1kUAAQSMESAAGdMKCvGqwObNmzU672zdOqZU3x4V23k4VcGbAj09y6+/Ls/U+x9O15lnnhlXRuuEbevk6ZoTqa1DZ9ZhMuuPtZfJ74/f4bu4Fs5gCCCAQAQBAlAEID5GoCkErD0tN1z3de3fuUHfH1uqCwf5g5e7hw9D5ZUB/WNFtZ6ak6bT+g/Ty6++oXbBy97tfh08eLA2DB0+fDi0t8m6oqxLly5KSws+f4MXAggg4BABApBDGkWZ7hew9rZ88MEHevihB7R6zVqd2zdN/doWq2uOL/gkeKkk+Givgv0BrSvM0sKNpTr7rKG65/5facKECQm5gsu6xH779u2hQPTFF1+cdIl9ixYt3N8wZogAAo4WIAA5un0U71aBXbt2adasWVqxfJm2bd2k4uKi4H17Wqhbz74aOuxsjR8/PhQ4TJm/dYm9FYJqDpVZe4NqDpXl5uYmJKCZYkMdCCBgpgAByMy+UBUCjhWwzhOyriSrCUPWnqKuXbuGbr7IJfaObSuFI+A6AQKQ61rKhBAwS+DYsWO1YWjv3r0nXWKfmckT683qFtUg4B0BApB3es1MEUi4gPU8shMvsbcex2GdRG0dLsvJyUl4fRSAAALeESAAeafXzBQBowSsk7737NlTu3eoqqoqFISsQGTdiLG+O1kbNRGKQQABRwoQgBzZNopGwH0Chw4dqg1D1uX21vlC1p4h6/yh9PR0902YGSGAQEIFCEAJ5WfjCCBQl0BpaWltGLKuLrMOj9UcKrPuTM0LAQQQaKwAAaixgqyPAAK2CliHxk68xD4lJaX2UBmX2NtKz+AIuFqAAOTq9jI5BNwncOIl9kVFRbWHyay7UVvhiBcCCCAQjQABKBollkEAASMFrABUc78h64Tq9u3b1x4q4xJ7I1tGUQgYI0AAMqYVFIIAAo0RsC6x37lzZygQWY/oyMrKqj1U1qZNm8YMzboIIOBCAQKQC5vKlBDwuoB1ib1108WavUMVFRW1e4asp9hzib3XvyHMHwGJAMS3AAEEXC9gPbm+JgwdOHBAnTp1CgUiLrF3feuZIAJhBQhAYWn4AAEE3ChgXWJf8xR765CZdYl9zbPKWrZs6cYpMycEEKhDgABUBwpvIYCANwSsS+x37doV2jtUUFCg5OTk2kNl1iX2fr/fGxDMEgEPChCAPNh0powAAnUL7N+/v/ZQmfUQV+vS+u7BR3NYd6VOTU2teyXeRQABRwoQgBzZNopGAAG7BYqLi2vD0O7du0OX2FuP5rD+WFeY8UIAAWcLEICc3T+qRwCBJhCwriKrucTeOpnauseQtWfICkPWJfY+n68JqmATCCAQTwECUDw1GQsBBFwvEAgETrrE3rr/UM1J1NYl9tZ5RLwQQMB8AQKQ+T2iQgQQMFjgyJEjtYfKrHOIrEvsa55in5GRYXDllIaAtwUIQN7uP7NHAIE4CliX2O/YsSMUiKy/W7VqVXuozPrZhNeCBQv0t7ff0NzZ07T58206WnRc1dUBtcrOVJ/TumvMuAv1jauv01lnnWVCudSAgG0CBCDbaBkYAQS8LGDdjbrmEnvrvCHrkvqak6itZ5Y15SX2Vi1vv/22HvjZT1VRckBfHVSmEb2k03J9yg7upAoEG3WkRMrfG9DCz316b1WKslq214MPParLLrvMy21k7i4WIAC5uLlMDQEEzBGw7kBdczdq67CZdd6QFYisS+3tvMTeugv2N772Ve3ZulJ3X1Sq0X2TokKZvrZKj3ycpiuvuUk/e+B/uPItKjUWcpIAAchJ3aJWBBBwhUBJSUltGLL2ErVr1672UFnz5s3jNsc9e/Zo1MhhuqjPYd11YVVwr1NsV6tVVgV039tJemtRiaz7InH5f9xaw0AGCBCADGgCJSCAgHcFKisrT7rEvlmzZrWHytq2bdvgS+wPHTqk1q1b62dXpOjmMdHt9QnXhUc/rNLUNdlatWZjaMxwy/E+Ak4SIAA5qVvUigACrhawLrEvLCys3TtknVRdc96QdXVZtJfYl5WVhfb8dE3epEeua1z4qQF/6B8+bSobpOmz5kddR826/I2AiQIEIBO7Qk0IIIBAUODo0aO1YWjfvn2y7jNUc4m9taco3Ou+//ypCj+dovu/UhpukQa9/50XU3TBNT/Vvff9rEHrsxICJgkQgEzqBrUggAACYQSsvTonXmJvPbm+Zu+Qdair5rV3717169NDH99VrbYtojvnp6wySUWlqcrJOl4zTJ1/7zxYrcufTNaWgi9kbZ8XAk4WIAA5uXvUjgACnhSwLmu3nk9mXVVmPcXeenX/16M5nnn6Ke2Y97h+cVlV6P1o/ufeNyYo2V+tB6+aFXHxn76ZonO+/gv9+Mc/ibgsCyBgsoDf5OKoDQEEEEDgywLWPYSsc4Ly8vJ0/fXXa+LEibLuOr106VK99PyzumpYxZdXCvPOjLU9tGBT1zCffvntq4aW6bWXpnz5A95BwGECPLTGYQ2jXAQQQOBUAesQmPUnNzdXJSXFOr1TdP9tu+9YM23ek6OBnQtPHTLs78N6+LT5xW2y7mWUnZ0ddjk+QMB0gej+X2L6LKgPAQQQQECrV69Wv05pUUtMmTVMk8Z8FvXy1oJJwXsJ9eqQobVr18a0HgsjYJoAAci0jlAPAggg0EAB67ygji2ro1r79YUD9dUhG5WWEv25QjUDdwo+1szaFi8EnCxAAHJy96gdAQQQOEGguLhYGSmRA9Dne1vpyPE0DeoS/aGvEzajZqkBWdvihYCTBQhATu4etSOAAAInCGRmZqqkov4bH1ZU+fVs8NDXDeeuknX5u/Un+DB4VVX7Qz8H78UY8VVc7uexGBGVWMB0AU6CNr1D1IcAAghEKdCrVy9tO1D/vX92HMjWm0sGhv6cOuzriwbp7R+8riHd95z60Um/F+yrVs+ePU96j18QcJoA9wFyWseoFwEEEAgjYF2Z1bF9W332oF8pSXUHocoqn/YdyzxphF++PU5Z6eW6+5L5wZshlig1OfxhtKLSgM75dbUOHj6mtLToT7g+aYP8goABAuwBMqAJlIAAAgjEQ8C6LH3QgL5alL9Ro/vWfSgsOSmgDi2LTtpcemqlMtPKv/T+SQv965dPNlYrb8RZhJ+6cHjPUQKcA+SodlEsAgggUL/AtyZ9T3/9NKP+hRrx6ZufNdNNk29vxAisioAZAhwCM6MPVIEAAgjERcC6OqtX9056/tvH1b9jfP8b99Ot1brr7Wzlb92p1NTUuNTLIAgkSiC+/+9I1CzYLgIIIIBASMC6EuzXv/md7n0nTZVVUVzSFaVbWUVA909N0+8efYLwE6UZi5ktQAAyuz9UhwACCMQs8N3vfldnjJioO1+Nz2me1cHr5E//zzKdf8k1uvbaa2OuhxUQMFGAQ2AmdoWaEEAAgUYKlJWVafiwM9Syepv+33f+7xEWDRmyvDKgW1/ya095e63bkC/rQay8EHCDAN9kN3SROSCAAAKnCFiXqK9YvUHNu43SpOdTg5e+x344bM/hgG54LlXt+1+oNes2EX5OMeZXZwsQgJzdP6pHAAEEwgr4fD6998E0nXf57frK75P0xLSADhVHDkL7g2Hp4Q+lSx9P0tdu/Klef3OqkpPjczgtbLF8gEATC3AIrInB2RwCCCCQCIH8/Hw99F+/1Jtvva2zT8vQiK5F6tnOp+xmvtCjMI6WBJQffDTY4m2ZWlFQFjrX5977f6lu3bololy2iYDtAgQg24nZAAIIIGCOQFFRkaZNm6a5s2do4/pVOnL4sALBB4C1zslRv9MHa/R543TRRRcpPT3dnKKpBAEbBAhANqAyJAIIIIAAAgiYLcA5QGb3h+oQQAABBBBAwAYBApANqAyJAAIIIIAAAmYLEIDM7g/VIYAAAggggIANAgQgG1AZEgEEEEAAAQTMFiAAmd0fqkMAAQQQQAABGwQwARJAAAAGeElEQVQIQDagMiQCCCCAAAIImC1AADK7P1SHAAIIIIAAAjYIEIBsQGVIBBBAAAEEEDBbgABkdn+oDgEEEEAAAQRsECAA2YDKkAgggAACCCBgtgAByOz+UB0CCCCAAAII2CBAALIBlSERQAABBBBAwGwBApDZ/aE6BBBAAAEEELBBgABkAypDIoAAAggggIDZAgQgs/tDdQgggAACCCBggwAByAZUhkQAAQQQQAABswUIQGb3h+oQQAABBBBAwAYBApANqAyJAAIIIIAAAmYLEIDM7g/VIYAAAggggIANAgQgG1AZEgEEEEAAAQTMFiAAmd0fqkMAAQQQQAABGwQIQDagMiQCCCCAAAIImC1AADK7P1SHAAIIIIAAAjYIEIBsQGVIBBBAAAEEEDBbgABkdn+oDgEEEEAAAQRsECAA2YDKkAgggAACCCBgtgAByOz+UB0CCCCAAAII2CBAALIBlSERQAABBBBAwGwBApDZ/aE6BBBAAAEEELBBgABkAypDIoAAAggggIDZAgQgs/tDdQgggAACCCBggwAByAZUhkQAAQQQQAABswUIQGb3h+oQQAABBBBAwAYBApANqAyJAAIIIIAAAmYLEIDM7g/VIYAAAggggIANAgQgG1AZEgEEEEAAAQTMFiAAmd0fqkMAAQQQQAABGwQIQDagMiQCCCCAAAIImC1AADK7P1SHAAIIIIAAAjYIEIBsQGVIBBBAAAEEEDBbgABkdn+oDgEEEEAAAQRsECAA2YDKkAgggAACCCBgtgAByOz+UB0CCCCAAAII2CBAALIBlSERQAABBBBAwGwBApDZ/aE6BBBAAAEEELBBgABkAypDIoAAAggggIDZAgQgs/tDdQgggAACCCBggwAByAZUhkQAAQQQQAABswUIQGb3h+oQQAABBBBAwAYBApANqAyJAAIIIIAAAmYLEIDM7g/VIYAAAggggIANAgQgG1AZEgEEEEAAAQTMFiAAmd0fqkMAAQQQQAABGwQIQDagMiQCCCCAAAIImC1AADK7P1SHAAIIIIAAAjYIEIBsQGVIBBBAAAEEEDBbgABkdn+oDgEEEEAAAQRsECAA2YDKkAgggAACCCBgtgAByOz+UB0CCCCAAAII2CBAALIBlSERQAABBBBAwGwBApDZ/aE6BBBAAAEEELBBgABkAypDIoAAAggggIDZAgQgs/tDdQgggAACCCBggwAByAZUhkQAAQQQQAABswUIQGb3h+oQQAABBBBAwAYBApANqAyJAAIIIIAAAmYLEIDM7g/VIYAAAggggIANAgQgG1AZEgEEEEAAAQTMFiAAmd0fqkMAAQQQQAABGwQIQDagMiQCCCCAAAIImC1AADK7P1SHAAIIIIAAAjYIEIBsQGVIBBBAAAEEEDBbgABkdn+oDgEEEEAAAQRsECAA2YDKkAgggAACCCBgtgAByOz+UB0CCCCAAAII2CBAALIBlSERQAABBBBAwGwBApDZ/aE6BBBAAAEEELBBgABkAypDIoAAAggggIDZAgQgs/tDdQgggAACCCBggwAByAZUhkQAAQQQQAABswUIQGb3h+oQQAABBBBAwAYBApANqAyJAAIIIIAAAmYLEIDM7g/VIYAAAggggIANAgQgG1AZEgEEEEAAAQTMFiAAmd0fqkMAAQQQQAABGwQIQDagMiQCCCCAAAIImC1AADK7P1SHAAIIIIAAAjYIEIBsQGVIBBBAAAEEEDBbgABkdn+oDgEEEEAAAQRsECAA2YDKkAgggAACCCBgtgAByOz+UB0CCCCAAAII2CBAALIBlSERQAABBBBAwGwBApDZ/aE6BBBAAAEEELBBgABkAypDIoAAAggggIDZAgQgs/tDdQgggAACCCBggwAByAZUhkQAAQQQQAABswUIQGb3h+oQQAABBBBAwAYBApANqAyJAAIIIIAAAmYLEIDM7g/VIYAAAggggIANAgQgG1AZEgEEEEAAAQTMFiAAmd0fqkMAAQQQQAABGwQIQDagMiQCCCCAAAIImC1AADK7P1SHAAIIIIAAAjYIEIBsQGVIBBBAAAEEEDBbgABkdn+oDgEEEEAAAQRsECAA2YDKkAgggAACCCBgtgAByOz+UB0CCCCAAAII2CBAALIBlSERQAABBBBAwGwBApDZ/aE6BBBAAAEEELBBgABkAypDIoAAAggggIDZAgQgs/tDdQgggAACCCBgg8D/B0YXCt2LWwTxAAAAAElFTkSuQmCC" /><!-- --></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="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>g1 <span class="ot"><-</span> <span class="fu">graph_from_literal</span>(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, C<span class="sc">-</span>A<span class="sc">:</span>B<span class="sc">:</span>E<span class="sc">:</span>H, D<span class="sc">-</span>B<span class="sc">:</span>E<span class="sc">:</span>F,</span> <span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a> E<span class="sc">-</span>C<span class="sc">:</span>D<span class="sc">:</span>F<span class="sc">:</span>H, F<span class="sc">-</span>D<span class="sc">:</span>E<span class="sc">:</span>G, G<span class="sc">-</span>F<span class="sc">:</span>H, H<span class="sc">-</span>C<span class="sc">:</span>E<span class="sc">:</span>G<span class="sc">:</span>I,</span> <span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a> I<span class="sc">-</span>A<span class="sc">:</span>H)</span> <span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(g1)</span></code></pre></div> <p><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/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAJAoAMABAAAAAEAAAJAAAAAAC2FqgIAAEAASURBVHgB7J0HeJzFtf5fWVaxJMu9G/fee+8NN9wbkARSSEIgyZ/0G3KTm4QQkkAgCaHkhgDhBnA3xhhjW+69996Ne7ckq0v/fQfWWLZWWklbvvLO8/jR7ldm5vzO592zM6dE5Hka1ERABERABERABETARQTKuEhWiSoCIiACIiACIiAChoAMID0IIiACIiACIiACriMgA8h1KpfAIiACIiACIiACMoD0DIiACIiACIiACLiOgAwg16lcAouACIiACIiACMgA0jMgAiIgAiIgAiLgOgIygFyncgksAiIgAiIgAiIgA0jPgAiIgAiIgAiIgOsIyAByncolsAiIgAiIgAiIgAwgPQMiIAIiIAIiIAKuIyADyHUql8AiIAIiIAIiIAIygPQMiIAIiIAIiIAIuI6ADCDXqVwCi4AIiIAIiIAIyADSMyACIiACIiACIuA6AjKAXKdyCSwCIiACIiACIiADSM+ACIiACIiACIiA6wjIAHKdyiWwCIiACIiACIiADCA9AyIgAiIgAiIgAq4jIAPIdSqXwCIgAiIgAiIgAjKA9AyIgAiIgAiIgAi4joAMINepXAKLgAiIgAiIgAjIANIzIAIiIAIiIAIi4DoCMoBcp3IJLAIiIAIiIAIiIANIz4AIiIAIiIAIiIDrCMgAcp3KJbAIiIAIiIAIiIAMID0DIiACIiACIiACriMgA8h1KpfAIiACIiACIiACMoD0DIiACIiACIiACLiOgAwg16lcAouACIiACIiACMgA0jMgAiIgAiIgAiLgOgIygFyncgksAiIgAiIgAiIgA0jPgAiIgAiIgAiIgOsIyAByncolsAiIgAiIgAiIgAwgPQMiIAIiIAIiIAKuIyADyHUql8AiIAIiIAIiIAIygPQMiIAIiIAIiIAIuI6ADCDXqVwCi4AIiIAIiIAIyADSMyACIiACIiACIuA6AjKAXKdyCSwCIiACIiACIiADSM+ACIiACIiACIiA6wjIAHKdyiWwCIiACIiACIiADCA9AyIgAiIgAiIgAq4jIAPIdSqXwCIgAiIgAiIgAjKA9AyIgAiIgAiIgAi4joAMINepXAKLgAiIgAiIgAjIANIzIAIiIAIiIAIi4DoCMoBcp3IJLAIiIAIiIAIiIANIz4AIiIAIiIAIiIDrCMgAcp3KJbAIiIAIiIAIiIAMID0DIiACIiACIiACriMgA8h1KpfAIiACIiACIiACMoD0DIiACIiACIiACLiOgAwg16lcAouACIiACIiACMgA0jMgAiIgAiIgAiLgOgIygFyncgksAiIgAiIgAiIgA0jPgAiIgAiIgAiIgOsIyAByncolsAiIgAiIgAiIgAwgPQMiIAIiIAIiIAKuIyADyHUql8AiIAIiIAIiIAIygPQMiIAIiIAIiIAIuI6ADCDXqVwCi4AIiIAIiIAIyADSMyACIiACIiACIuA6AjKAXKdyCSwCIiACIiACIiADSM+ACIiACIiACIiA6wjIAHKdyiWwCIiACIiACIiADCA9AyIgAiIgAiIgAq4jIAPIdSqXwCIgAiIgAiIgAjKA9AyIgAiIgAiIgAi4joAMINepXAKLgAiIgAiIgAjIANIzIAIiIAIiIAIi4DoCMoBcp3IJLAIiIAIiIAIiIANIz4AIiIAIiIAIiIDrCMgAcp3KJbAIiIAIiIAIiIAMID0DIiACIiACIiACriMgA8h1KpfAIiACIiACIiACMoD0DIiACIiACIiACLiOgAwg16lcAouACIiACIiACMgA0jMgAiIgAiIgAiLgOgIygFyncgksAiIgAiIgAiIgA0jPgAiIgAiIgAiIgOsIyAByncolsAiIgAiIgAiIgAwgPQMiIAIiIAIiIAKuIyADyHUql8AiIAIiIAIiIAIygPQMiIAIiIAIiIAIuI6ADCDXqVwCi4AIiIAIiIAIyADSMyACIiACIiACIuA6AjKAXKdyCSwCIiACIiACIiADSM+ACIiACIiACIiA6wjIAHKdyiWwCIiACIiACIiADCA9AyIgAiIgAiIgAq4jIAPIdSqXwCIgAiIgAiIgAjKA9AyIgAiIgAiIgAi4joAMINepXAKLgAiIQPEJXLp0Ca+++iomjRuB5o3rIDEhFpGRZVApMQ4d2zTBV7/yIGbMmIFbt24Vv3PdIQJhIBCR52lhGFdDioAIiIAI2IDAiRMn8Ktf/AwfzP8AQ9tEo3ejNLSoHYG6lSMQFw0kpwMnL+dh+8lcrDoaj+0nsvHtx7+DH//kv1CpUiUbSKgpupWADCC3al5yi4AIiEARBN5660388P89iUd65eKrffOQEBtRxB3A2Wt5+NuyKKw5GoNZcxegR48eRd6jC0QgHARkAIWDusYUAREQAYsT+OH/+y7mTH8Tb309C/WqFN9bYv3hHDz8ahYWLPgQo0aNtri0mp4bCcgAcqPWJbMIiIAIFEKAxs+sd/+Bed+LQMX4old9fHV14GwuRv85C++++x6mTp3q6zIdF4GwEJABFBbsGlQEREAErEngzX/9C4899g1s/nV0qYwfr3T7zuTiK/8siyXLVqNTp07ew/orAmEnIAMo7CrQBERABETAGgSOHTuGbp3bYc4TWbivBNtevqRYsicHL6ysiZ17DiE2NtbXZTouAiElUPyN3ZBOT4OJgAiIgAiEisAvn/6Jx+E5xy/jJyU9CqeuJCIr57OvkfTMSPBfQW1om0jUTbiBN974Z0GndUwEwkJAK0Bhwa5BRUAERMBaBC5cuIDmTRtgzc/zEB9TsN9PTm4E/rG8Mz7a0Qxt6170GEo3PFFf5bHvbDXcyojC7yYnoXPDcwUKtsMTJv/jeVVw+PgZREQU3H+BN+qgCASJQNkg9atuRUAEREAEbERg5syZuL9dlMf4ySpw1hlZkZ6oronIzI7Evx6bh+qJXyQ8PHstARP+Mg1pWb6/UjrUL4PI3FRs2bIFXbt2LXAMHRSBUBLQFlgoaWssERABEbAogaRFH6BvY09WQx/tb4u7e5Id1jKrPHcaP7y8dqUU/GbicuR8vh3mowv0bZqNpKSlvk7ruAiElIAMoJDi1mAiIAIiYE0Cu3bvRotaBW9Ncevrnys7oVG1q2h738UCBRjW9ij6tzxZ4DnvwRY1srBr20bvW/0VgbAS8L1eGdZpaXAREAEREIFQEjh/8RrqVCrYADp1pYJn66ss6lROvmdKl5PL4XJy3O3jleLTUaNC6u33d76o6+l//rbCjaQ7r9drEQgmAa0ABZOu+hYBERABGxBgScj0zCzERhdsAHkNnMrxafdIk5NbBrM3t8LI57+MH757PzI8PkK+Wqyndlhauu9tNl/36bgIBIOADKBgUFWfIiACImAjAozKKhcThbTMgmtjN65+1Uhz9nr5e6Tias+X++w0xwe3Pu4pm3Hznmu8B25leOqJxcd73+qvCISVgAygsOLX4CIgAiJgDQL16tTAiUsFG0CVE9I9UV8pOHahEugPdHdLiMk0h2Kjsu8+le/9CU/V+AaNmuQ7pjciEC4CMoDCRV7jioAIiICFCHTu0hU7T+f6nNFTw9fjcko8Zm5s7fOaok7sOhuLbj37F3WZzotASAjIAAoJZg0iAiIgAtYmMGzkOKw84nt7amqPvbi/7WH88aPe2HikTj5h0rKizPuIiIJXkHgyNzcPKw/kYvDgwfnu1RsRCBcBZYIOF3mNKwIiIAIWIpCcnIx6dWti0Q9yPNtd925zcaoeX2lM96wAvbq0G5rXuow2nmzQKRnR2PNpdTSreQXfHbYRVRLudZTmvcv35eDVLU2wefs+vlUTgbATkAEUdhVoAiIgAiJgDQI/+sH3cXHbG/j12JwiJ3TjVgzoFF2/ynXExRTu+8POxr0cjV/+8V+YOHFikX3rAhEIBQEZQKGgrDFEQAREwAYELl26hNYtGuPVL2WgU4PAeUi8tSYPSWdbY+WaTYiM9B0mbwNEmqKDCATuCXcQFIkiAiIgAm4jkJKSgvXr1+NnT/8Pvv3vSBy76NshujhsFu70bH2tjMH0WfNl/BQHnK4NOgFlgg46Yg0gAiIgAtYmcOTIEaxduxYdOnTAmDFjEB9XDkMe/w4+/nG0x9en5L+Tl+zJwZNvZ+HkySOoUye/47S1iWh2biCgLTA3aFkyioAIiEABBDIzM7FmzRpw64vRWVWrVr191ccfL8TUyRPwt4dz0a9F8bet3l4LvL4qFnPnf4wePXrc7lcvRMAqBGQAWUUTmocIiIAIhJDA+fPnsWzZMtSrV88YKGXL3rshsGHDBjw0dTxaVEvFEwMy0KpO4atBLKmxyhPq/pdlsciKqoY3//0+unbtGkKpNJQI+E9ABpD/rHSlCIiACNieQG5uLrZt24Z9+/ahf//+qF+/fqEypaWl4bVXX8VLf34O5aMz0bdJBppXz0Hdyp7yGZ7aXikZwKnLnj5Px2D1IaB6zTr42S9+i759+2LJkiWYMmUKYmNjCx1DJ0UgHARkAIWDusYUAREQgTAQuHnzJpKSkhATE4MBAwYgLu6LKu5FTYeGE52klyUtxc5tG3H2zGnQOIpPiEejRk3RuXtfDBs2DC1btrzdFVeQUlNTlfzwNhG9sBIBGUBW0obmIgIiIAJBInDw4EHQIOncuTPatGkTpFHyd5udnY1Zs2aZLbYGDRrkP6l3IhBmAvdu+oZ5QhpeBERABEQgcAQyMjKwatUqXL9+HQ888AAqV64cuM6L6Il+RVxpWrp0KWrVqmVWnoq4RadFIGQEtAIUMtQaSAREQARCS+Ds2bPG0blx48bo1q1b2PLwrFu3DjTEBg4cGFoAGk0ECiFQuEt/ITfqlAiIgAiIgDUJ5OTkYOPGjcb44QpMz549w2b8kBCNr3PnzuHUqVPWBKZZuZKAVoBcqXYJLQIi4FQC3Oqio3NCQoKJ8rJKBJZ3NYpRYdHRnvAxNREIMwEZQGFWgIYXAREQgUARYGj75s2bzYrLndFYgeq/tP2sXr3aU1E+D/369SttV7pfBEpNQAZQqRGqAxEQAREILwGGo69cuRK3bt3CoEGDULFixfBOyMfoWVlZmDFjhlmZqlu3ro+rdFgEQkNAPkCh4axRREAERCAoBE6fPm1CzRndNW7cOMsaPxQ+KirKGD+MSqMxpCYC4SSgFaBw0tfYIiACIlBCAsyxs2nTJhw/ftys+jDM3C6Nq1WRkZHo06ePXaaseTqQgFaAHKhUiSQCIuBsAlevXsXcuXPNltekSZNMjh07ScyotBMnToCO0WoiEC4CWgEKF3mNKwIiIALFJEAH4j179phaXjQimjVrVswerHM5Q+LXrl2LyZMno6BCrNaZqWbiVAIygJyqWcklAiLgKAJ0cF6+fLnxnaGjc2Jiou3lozysS9arVy/byyIB7EdAW2D205lmLAIi4DIC3C5iTa2aNWtizJgxjjB+qEIaPkePHsX58+ddplGJawUCWgGyghY0BxEQAREogAAdnVlG4syZM8bRuUaNGgVcZe9DNO5YpJW+TNoKs7cu7TZ7GUB205jmKwIi4AoCly9fNkVEuerDlRInZ09msVRmru7Ro4crdCshrUFAW2DW0INmIQIiIAKGAB2dd+zYgY8++ghdu3Y11dSdbPxQaIbDHzp0CBcvXtRTIAIhI6AVoJCh1kAiIAIiUDiBlJQUU8A0IiLCVE7nqohbGn2Btm7diokTJ4a1cKtbeEtOQAaQngIREAERsAABGgAMC2/Xrh3at28PGkFua4sXL0alSpXMypfbZJe8oScgAyj0zDWiCIiACNwmkJmZaQyfCxcuYMiQIahatertc257wVB/RruNHDnS1RzcpvdwySsfoHCR17giIAKuJ0Cjh1/4jH5iFJSbjR8+DHFxcWCCxxUrViA3N9f1z4cABJeAVoCCy1e9i4AIiMA9BPjlvn37duzduxf9+vVDgwYN7rnGzQcWLVqEatWqoXPnzm7GINmDTEAGUJABq3sREAERuJPAzZs3jaMzK6MPHDjQrHrceV6vgdTUVMyePRujR48Gq9yriUAwCMgACgZV9SkCIiACBRBgqPf69evRqVMntGnTxpWOzgVgKfDQgQMHsG/fPowbNw5lyshbo0BIOlgqAjKASoVPN4uACIhA0QQyMjKwevVqXLt2DYMHD9aqRtHIzBXMhVSnTh106NDBzzt0mQj4T0Bmtf+sdKUIiIAIFJvA2bNnjaMzHXzHjx8v46cYBPv374+dO3caw7EYt+lSEfCLgFaA/MKki0RABESgeATo6LxlyxYcPHjQZHO+7777iteBrjYEuA3GrcOxY8dqy1DPREAJyAAKKE51JgIiIALA9evXjaMzV30GDBiA2NhYYSkhAZYG4VZYvXr1TJLIEnaj20TgHgLaArsHiQ6IgAiIQMkJ7N+/Hx988AFatGiB4cOHy/gpOUpzJzNiM1UA0wbcuHGjlL3pdhH4goBWgL5goVciIAIiUGIC6enpWLlyJVjPi47OFStWLHFfuvFeAnv27AHLhYwZM0ZbYffi0ZESENAKUAmg6RYREAERuJPAp59+ipkzZxqjh47OMn7upBOY161btzYdMXmkmggEgkDZQHSiPkRABETAjQRycnKwceNGHD9+3Kz61K5d240YQiIzt8LoTzVv3jzjD5SYmBiScTWIcwloC8y5upVkIiACQSRw9epVJCUlmdUe+qjExMQEcTR17SWwa9cunDp1CqNGjdJWmBeK/paIgLbASoRNN4mACLiZAP1RPvzwQxOVNHToUBk/IXwY2rZti6ysLNDZXE0ESkNAK0Cload7RUAEXEXg1q1bplI5MzvT0VnbMOFRPzNq0wCdMGECEhISwjMJjWp7AjKAbK9CCSACIhAKAidPnjRRXq1atTK1vFSfKhTUfY/BsPhz585h5MiRvi/SGREohIC2wAqBo1MiIAIikJ2dbep4rV27FsOGDUOXLl1UnNMCj0X79u2RlpZmMm1bYDqagg0JyACyodI0ZREQgdAQuHz5MmbPng0aQZMmTULNmjVDM7BGKZIAV+AGDhyIDRs2IDU1tcjrdYEI3E1AW2B3E9F7ERAB1xNg+QVGG7EQZ69evdCkSRPXM7EqgK1bt+LSpUsm67ZV56h5WZOADCBr6kWzEgERCBMBZnJevnw5aAQNGjRITrZh0oO/w7Lo7Jw5c8AtsaZNm/p7m64TASgRoh4CERABEficwLFjx7BmzRow1LpDhw7KM2ODJ4NbYQM8CRIXLlyIOnXqgAVo1UTAHwJaAfKHkq4RARFwNAHmlaGT8/nz5014e7Vq1RwtrxOF27x5MxgeT0d1NRHwh4CcoP2hpGtEQAQcS+DixYuYNWuWieyaOHEiZPzYU9WdOnXC9evXTcFUe0qgWYeagLbAQk1c44mACFiCAH1HduzYAWZ1ZimLBg0aWGJemkTJCERGRpqtsEWLFpmtsNjY2JJ1pLtcQ0BbYK5RtQQVARHwEkhOTsayZcvAL006OstvxEvG/n8ZFk9H9iFDhthfGEkQVALaAgsqXnUuAiJgNQKHDx82UUMNGzY0BTVl/FhNQ6WbDxNVMn/TiRMnSteR7nY8AW2BOV7FElAERIAEMjMzTUbnK1euYPTo0ahSpYrAOJBA2bJlzVbY0qVLTeJKbYU5UMkBEklbYAECqW5EQASsS4A1o7jlRT+f7t27g1+Sas4msG7dOrBoLbNFq4lAQQS0BVYQFR0TARFwBAE6Om/atAlcDejbty969+4t48cRmi1aiG7dupm0BqdOnSr6Yl3hSgJaAXKl2iW0CDifwI0bN5CUlIRy5cqZLRH+VXMXgbNnz5qVvylTpiA6OtpdwkvaIgnIACoSkS4QARGwG4EDBw5g48aNpnJ769at7TZ9zTeABJjZOycnB/379w9gr+rKCQRkADlBi5JBBETAEEhPT8eqVatw8+ZNEwZdsWJFkXE5AWb5njlzpsn1VLduXZfTkPh3EpAP0J009FoERMC2BM6cOWMyOicmJmL8+PGQ8WNbVQZ04lFRUcb4WblyJWgMqYmAl4BWgLwk9FcERMCWBLi9QUfno0ePmogfFsRUE4G7CXBlMCIiwjjD331O791JQCtA7tS7pBYBRxBg8cu5c+eCmZ0nTZpkSiA4QjAJEXACPXr0wMmTJ0HHaDURIAGtAOk5EAERsCWBvXv3YsuWLSavT4sWLWwpgyYdWgIMiadTNKPClAsqtOytOJoMICtqRXMSARHwSSAtLQ3Lly8HHZ4HDx6MChUq+LxWJ0TgbgJ8dmJiYtCrV6+7T+m9ywhoC8xlCpe4ImBnAvwFP2vWLFSrVg3jxo2T8WNnZYZp7jR8jh07ZpIkhmkKGtYiBLQCZBFFaBoiIAK+CWRnZ4NVvmkAsXp7zZo1fV+sMyJQBAEWSuXzRL8xbYUVAcvBp2UAOVi5Ek0EnECAxUuZ0blq1aro06ePMvo6QakWkIHPVHx8POgcreZOAtoCc6feJbUIWJ5AXl4edu3ahQULFqBTp05m5UflDCyvNttMkHXhDh06hIsXL9pmzppoYAloBSiwPNWbCIhAAAikpqYaR2fm+OGWV/ny5QPQq7oQgfwE6AvESMKJEyciMjIy/0m9czwBGUCOV7EEFAF7ETh+/DhWr16NNm3aoGPHjiZ5nb0k0GztRGDx4sUmazirx6u5i4AMIHfpW9KKQEAJXL9+3dTdYrmBKlWqlMo/h2UK1q1bZxLVMby9evXqAZ2rOhOBgggwrQJrhY0cOdL4mRV0jY45k4AMIGfqVVKJQFAIXL16FdOnT8f8Oe9h05btyMnOQmJ8FDIyc3AzNQu1alRBv/4D8eCXHsWwYcP8Xr2hHwadUmvXrm3ys9CgUhOBUBE4fPgwdu7ciQkTJqBMGbnGhop7uMeRARRuDWh8EbABgcuXL+P3z/wab/zrDQxoVRZDW6Sje+MyqJIQcXv2Obl5OHk5D2sO5WL+7ngkZyfgmef+bLLu3r7orhd0dN6+fTv27NljajQ1bNjwriv0VgRCQ2DRokUmv1Tnzp1DM6BGCTsBGUBhV4EmIALWJrB161aMHX0/BjZLx/cGZ6Nq+S+MnsJmvuFIDn6zIAb9ho7Di399HXFxcfkuT0lJMas+dD4dOHCgCUnOd4HeiEAICdDxfvbs2Rg1apTZzg3h0BoqTARkAIUJvIYVATsQ+OSTT/DQ1PF4cVo2+jYvfpQMV4W+8+8ILNmVDho8zLvCduTIEaxduxYdOnRAu3bt/N4qswMzzdG+BA4ePAjWmGOWcW2F2VeP/s5cBpC/pHSdCLiMADPl9uzZE/Oeika7+0rnF/H8wlwsP1Ub6zZux7Zt28AtNTo603FaTQSsRGDhwoWoVauWiUC00rw0l8ATKN2nWuDnox5FQAQsQODo0aMYPXIo/vG1qFIbPxTnRyPLoGP1ixg9Ygjo4ExnUxk/FlC0pnAPgX79+pkEnNeuXbvnnA44i4AMIGfpU9KIQEAIPPbVh/HkwEwMaVP8bS9fE/jt+BykXjxoClGq/pIvSjoebgIJCQno2rUrVq5cidzc3HBPR+MHkYAMoCDCVdciYEcCS5YswZkT+/BIn8B+PERERODXYzPx9M9+CBY3VRMBqxJo1aqVKZK6e/duq05R8woAgcB+wgVgQupCBEQgvARe/NPv8K2+6QVOYvm+Bhj34jQ0+sH/w+S/TsHqg/XMdZeTy+GFhT3N8cY//D5eTeqC5PToe/po6/ElalQ1G3PmzLnnnA6IgJUI9O/fHzt27MCNGzesNC3NJYAEZAAFEKa6EgG7E+CH/Zq1GzCyQ8EfDQNbncBDvT77Vfxov+2eyLBTRuSq5dPww5HrPSHyqWhW8woeH7wF5WMzC8Qxrt0tvPfOGwWe00ERsAoB1p9jTqAVK1aA+arUnEeg4E8558kpiURABPwgwND0jo3jEFPWd66fhJjPDJv4mKx7euSx+M/P33Py8wN9mpfBytVrfZ3WcRGwDIHWrVubFA0MjVdzHgEZQM7TqSQSgRIT2L9/P5pWTSvx/f7cWM2TSDEiL8eEwvtzva4RgXARoN8at8KYDPTmzZvhmobGDRKBskHqV92KgAjYkMC5M6dQLZ4OykV/NOw/W9WzUpTfmTkts+j7iKV6xRicO3dOxSdt+Iy4bcoVKlQwOYEYFTZ69Ggl7XTQA+Dfp5WDBJYoIiACvgmkp6eh/L2+ywXesP9sNdzKyF+0ND3Lv4+UcjFlkJ5esKN1gYPpoAiEkUDbtm1N+gaukDJCTM0ZBPz7tHKGrJJCBESgCAKJFSoj5WQRF31+ekKX/RjQ8kS+ixfsaJ7vva83yWm5SExM9HVax0XAUgS4FTZgwAB88MEHqFevHpgrSM3+BOQDZH8dSgIRCBiBJk2b4eT1/EVLA9b55x2xPtinF1PRoEGDQHet/kQgaAQqVqyI9u3bmwSJQRtEHYeUgAygkOLWYCJgbQLdunXDpmPBzX6781QemjWuh5iYGGvD0OxE4C4CLNybkZGBAwcO3HVGb+1IQAaQHbWmOYtAkAi0adMGORExOHLBtxF0K/Mzv5+rKeXumQV9ggo6fueFS/dFYvTYSXce0msRsAUBVojnVtimTZuQmppqizlrkr4JyADyzUZnRMBVBFj36MiRI+jesy/+szG/c7MXxNK9DfHW6g7m7duev6sO1DevmQn62fl9cSk5HicuV8RLi3oUmAk6IzsPc7dF4MuPfNXbpf6KgK0IVK5cGcwPtHr1alvNW5O9l0CEJ8OlUlzey0VHRMA1BDIzM8Holj179hjH5Fq1amHYkP6Y/90c1KnsOyFiSQD970pgf+4AzJ3/cUlu1z0iYAkC/LHAci7cEmvWrJkl5qRJFJ+AosCKz0x3iIAjCCQnJ4PFHg8dOmQiW+6///7beXl+9OOf4Wfv/glvfS0TkWUCYwQdu5iL15dHYv3mvziCn4RwLwHvVtjChQtRt25dxMUFN3DAvaSDK7lWgILLV72LgOUIXLhwAbt27cLZs2fRokUL0O8nPj4+3zy5MDx+zAjgwiq8+GDpF4nPX89Dr99k4H9+9Sv07NXLZNeVE3Q+5HpjQwKbN2/GtWvXMGzYMBvOXlOWAaRnQARcQIBL9idOnDCGT1paGpjYrXnz5oiKKtjXh0h4XcWKFZCXk4V9f4gp8UrQiUu5mPRyHv7718/ie9//ATZs2GDmMnjwYNSoUcMF9CWiUwnk5ORg9uzZpmhq48aNnSqmY+WSAeRY1UowEQDo38OQXfr3MHkbfRbq16/vdzp/fsCPHX0/Uj7dhD9OykS1xOJthy3dm4On50Thd8+9iMe++c3bKjl58qTJp8K8KpwTE82piYAdCVy8eBGLFi3ClClTEBsba0cRXDtnGUCuVb0EdzKBlJQU499z8OBB3HfffWbFp3r16iUSmdth//Or/8bf/vJnTO0OPNw9t0jn6A1HcvCP1eXwaUp5vPnv99GnT597xuYck5KSEB0djYEDB+rL4x5COmAXAhs3bgR96oYMGWKXKWueHgIygPQYiICDCPDXKP17Pv3009v+PYFK23/69Gm88Mff4z//+T/UrlQG3RpkomGVLCTERCAzB578P3k4ciUOaw7loFr1mvjuUz/DI488Uug2G7fmtmzZYhyxuSXGCDQ1EbAbgezsbLMVxkSiDRs2tNv0XTtfGUCuVb0EdwoBrtB4/XuYnI3+PXRuLsy/pzSy88Oefjz8t3vnVpw5fcJjuNRGjVp10bJ1O+Pg3KRJk2INQYNt+fLlJr9Kx44dtSVWLHq62AoEzp8/j6VLl2LSpElazbSCQvyYgwwgPyDpEhGwIoGsrCxwi4srPgzDpS8Nf32G0p+GkWRbt27FAw88UGpEt27dMltinP+gQYMUWlxqouog1ATWrVuH9PR08/yGemyNV3wCMoCKz0x3iEBYCdB3hk7NdG5mDhIaPiX17ymtIKdOncLevXsxYoQnZD4AjatZ27ZtM4kZWXKA8qmJgF0IcHV05syZ6N27t8mtZZd5u3WeMoDcqnnJbTsCly5dMqs99MVhCDvz95QvXz6schw7dgxHjx7F0KFDAzoPriwtW7bMZNnt0qULmHhOTQTsQODcuXNmJZNRYXTw9zYWUaW/G3+40FePK7j8/9u0aVP06NHjdhJS7/X6G3wCMoCCz1gjiECJCXBFhCHj3OZilInXv+fOD9YSdx6AG5lF+syZMyaKKwDd5euCeYjoF8QvCjpIB8qZO98geiMCQSCwZs0aMIVE//79sX79evzlz8/h448Xo0mdcmhaPQeVYtMRE5mHlKxonLgajY2HUtGqeRM8+tiTePTRR1Gu3L2FhoMwTdd3KQPI9Y+AAFiRAJfS6d/DUhXMmOz177HaSsi+fftw5coV9O3bNygYaQDu3LnTGID8MmEOIzURsDoBGu1///vf8eHcGThycBe+0ScTYzqVQWK5gvNd5eTmYd3hXLy7qRx2ny2L5198GQ8++KDVxbT9/GQA2V6FEsBJBOgITP8eFidlSDgNn5o1a1pWRK5MMfKsZ8+eQZ0jy3cwZxCdvLt3764tsaDSVuelJbBixQpMmzwWj/TIwDcHwPO8Fmz4FDTOjpO5+MmsaIyd/FWPIfTXgi7RsQARkAEUIJDqRgRKQ+Dy5ctmtYfbXawuTf+exMTE0nQZknvpsMyl/q5duwZ9PPpQ8IuFRiITzoXb/ynoAmsAWxJYvnwZRg4fhv/9Whn0bhZZIhluZeShzX9lYEC/nkhavkYGf4koFn2TDKCiGekKEQgKAW7v0KGZqyjXr183Rk+rVq3yOU4GZeAAdrpp0yaTb4i5e0LVuC24fft2k126UaNGoRpW44hAkQRYHJXJEOc9FY1295XOcT87Jw8PvR6JXsO/gr/87dUix9YFxSdQtvi36A4REIHSEKB/D52H+UXOZIXc5uIXudX8e/yRkbKE2mGTjuDcFmTSOUaLcfstMrJkv7T9kVHXiIA/BBjZxbp5bz7m+T9dSuOH45WNjMDb38jB+L//H97q3N04R/szD13jPwGtAPnPSleKQKkIcOuGOXPo38Mq6DR87F76YdWqVahWrRpatmxZKjYluZmFXjk+V88Yhl+hQoWSdKN7RCAgBL79za8h++gMPD3aUxcmgG3fmVx8/e1yOHzstLZ9A8iVXZVujS7Ak1F3IuBEAlevXjW+KzNmzAD9WMaOHYv777/f9sYPdcUVoLJlw7OQzFQA9AXituG8efNw+PBhJz4+kskGBFgGY/r77+GJgdkBn22rOmXQs3EuXnv1lYD37fYOw/PJ5Xbqkt8VBLz+PdeuXTM1rhjWypB2J7VwGkBejjSAuCW2ZMkSsyXGLLzhMsq8c9JfdxH499tv4f52kagYX/DqT2pGFN5Z0x4vfNwLEcjDpG77PFtcufC4AeJmWoyngHB9/Gz0akzuvq9AcA93y8Av/vEyfvyTnxZ4XgdLRkAGUMm46S4RKJAADQKuRNC/hz493OZiYVA7+vcUKOBdB61gAHFKlStXxoQJE8AEdHPmzDErQzymJgKhIDB/7vv4VsdMz1AF+6LFx2Th24O34OOdTXE5JQ7PTknKN61Fu5rgSorv5IedG5bBzRtXwczrcvzPh65Ub2QAlQqfbhaBzwgwazH9e5gYkHW5+vTpg9q1azsej1UMIIKmQ/nAgQONg/mCBQtMNE6LFi0crwMJGF4CTAOxZfte/HNiwcbPnbOLi/EYSR4D6M7GVaCBrY4jK7twj5TuTaKMgS8D6E56pXstA6h0/HS3ywlwe4th7MePH0fjxo0xZswYVKxY0TVUrGQAeaEzjxKNUO+WGLNU0zhSE4FgEGDurlpVyiGmbFaJut9yvDYuJcdhZPsjhd7fqNItj3F/sNBrdLJ4BGQAFY+XrhYBQ+DTTz81hg/LQLRu3RrTpk1DbGys6+hY0QCiEmiEjh8/HuvWrcPs2bPNlljVqlVdpx8JHHwCN27cQGIcV3/8M4DoD/Te+jZmYlc8q0Ef7WiG7w7bUOREE8vl4dLVS0Vepwv8JyADyH9WutLlBLjU7fXvIQr69zCay805aKxqAFE/dITu16+fqVa/cOFCdO7c2RirPKcmAoEiwP//mdmefSw/W2SZPNSokGKujvI4QifGZvh1Z5bHv7pspL6y/YLl50Wi6ScoXeZeAunp6ca3hz4+XEVg4r26deu6F8gdklvZAPJOk1uTzFXExImsXM+iqk6LxvPKqr+hJ1ClShVcS/Fv9Yezi43KxqBWJ25PtFmtyzh7reiyNzfSIj1bu7Vu36cXpScgA6j0DNWDQwkwwR79e7yRF6NHj0alSpUcKm3JxLKDAUTJWFeN+Zc2btx4e0uMfkJqIlBaAgx2uJWe6wlnz/NZ7b2wMdrdd9GTOfpiYZeYc0euxKF/GBKOFjkxG18gA8jGytPUg0OAqwQ0fC5dumS2TKZOnRrycg/BkSzwvdrFAKLk3Kro1auXic5btGgROnToAJbViIjwv1J34AmqR7sT4PPTq0cXrD20ESPaFx0JVhJ5c3PzsOFQGt7yOPSrBY6ADKDAsVRPNiaQm5uLI0eOGMOHr+nfM2zYMFf79xSlTho/NCrsZkA0aNDAbGV6a4kNGDDAlQ7sRelX5/0nMPXhr2HGy7s8BhBzAflu6VlRuH4rFjm5EaAvkL9t+f5ctG7ZypTQ8fceXVc0gcITDxR9v64QAVsTYGkKVhb/z3/+Yxycu3fvjilTpoD5Y9zs3OyPUu20+nO3PAkJCSZlAbc0GSXGUgZqIlASAlwx5hbrhiNZOHYxt8AuGPn10qIe2HemGtIyo/Dbef1x7npCgdcWdPD11bF48qmfFXRKx0pBQMVQSwFPt9qXAENXma2Zqz5cEeCKjzIHF0+fKSkp+OCDD/Dwww8X70aLXc2SJStWrDDbnR07drTdipbFcLpiOnme7IXM/bVjxw5TD699+/aY/8E8LPrPM/jno4GtB/bxzhy8tqketu8+qB9lAX66ZAAFGKi6szaBs2fPmm2uixcvmiKarCMVF5c/M6u1JbDO7Ogk/sknn4A+UnZvqampWLZsmSlZwmzSeibsrtHgzN+bCmPnzp0mkpB+ZPXr1zdGM1eTe3XviBENj+Pr/QIzPleUHny9LD74aKmJPg1Mr+rFS0A+QF4S+utYAvTpOXr0qFnxycrKMqs9rCKugpmlU7mdt8Duljw+Ph6jRo3Ctm3bTC0xGkF16tS5+zK9dymBzMxM7N+/33yGMOyd2cXvLnXD1AofLlyKzh1ao3JcKsZ3KZ1D9MWbeRjyXCbe+OcrMn6C9NzJAAoSWHUbfgL8RcYPrT179pjMwF26dMF9992nLY4AqcZJBhCRsGAtnxF+sXE1qHnz5iZ5olML2QboMXB0N6zxx61yfo7ws2PEiBGgAeSr8dnZsn2PyRN2+GIEfjKyZG62XPn5yj8j8fLfXsTXvv51X8PpeCkJaAuslAB1u/UI3Lx503xoMWszl6fp31PYh5b1JLDHjFgOhFsBXDlxWuMXH40gbnkMHjwYXCFScw8BfoYwFQZ9BJs0aWI+Q+jo7G/jVvvI+weiZtRZ/PKBLNSq6F+qheycPPxnfR5eXlYWr7z+pgnI8HdMXVd8AjKAis9Md1iUACN5+KF17tw5tPQkDGvTpo18OYKoqxMnTuDgwYOmHEgQhwlb13R0pZMrVxCZPbpevXphm4sGDg0B1vajzmnc0z+QnyHlypUr0eDMIP+H557FSy++gMGtIzG+Qzq6NCyD6LL3GkOHzudi8Z4ITN9cFq3bdsKf//qqyraUiHrxbpIBVDxeutpiBOjfw0zNXKbmlhdXe1gNXP49wVcUfx2zEjZXSJzcaFgnJSWBJTW6detmtsqcLK8bZeOKDQ2fq1evms8Q/oCKiooKCAoGC7z55r/w9huv4OCRk2hQIx6Vy5fxVI+PQHJ6Ho6eS0MFz+rS6DHj8PXHHjcJOgMysDopkoAMoCIR6QIrEvA6JfLXOZemmdHXG41hxfk6cU4HDhzAhQsXzOqIE+W7Uyb+mmeoPP/S4Ctfvvydp/XahgS4wkcDnoYPfzwxlL1p06ZBCTXnVurbb7+NSZMmmTGZZZ4BGfzs4hYba9WphZ6AnKBDz1wjloJAcnKyWe05dOiQ2ZJgNXYWKFULPQGnOUEXRjA2NhbDhw83W6xz5841UUANGzYs7BadsygBrhrTP5CGT3R0tFlxYS6wYGY05w8F5hnz/lizKBrXTUsGkOtUbk+B+QFC/x4uVTNL8+TJk+WYGmZVuskA8qLmFmvNmjXNlhifxR49egRlxcA7nv4GjgBXXBjNxc8RGiMFhbIHbrT8PdGnSGkV8jOxwjttgVlBC5pDgQT4S42OtvzAYlQOt7kYmhyovfkCB9VBvwls2bLF/Gru3Lmz3/c45UJuwa5cuRKMFmJOqQoVKjhFNMfJwc8ObpXv27fPGCFMXhjqVWOuGrLMzt25gxwH22YCaQXIZgpzw3T55UL/En5osWYT9+aDvUTtBq6BlpErQCWNkAn0XELdH7dOhg4dar5UWQ6EVebpy6FmHQLcLmeaBjrr04F9/PjxZgsq1DOkf9G1a9fMymGox9Z4hROQAVQ4H9edZcSC17mVS8Z09uQHe6NGjYK6R07QrC3FaC6GVjPpGH9ZV69e3XU6sIvAbtwCu1s3DJWuUaMGvJXlaQgpAvFuSqF9z0gu+vewxhujuViqJZyGOoul1qpVS9GDoX0M/BpNW2B+YXL2RayL9b//eB3v/+dNnDx9Fk1rl0MVT6HiGE8UaHJ6GRy/kImUDGDUyOH42mNPYMCAAQEFwvG5zcUPCm5xMfcGV37UrE2AUVH8YKfO3N74Y2H16tVgHhka7qwyrxZaAsz/RcOHOuBnCI1TrtSFu61atco8D9zCV7MWARlA1tJHSGfDApC//fUv8fprr+KBjmUwrkMW2teL8PxSuTdR17nreVi8OxfvbY5FpRoN8Je//6/Z0y7phBmC6vXv4Ty8/j1W+MAqqUxuu4+rHoyE4vaC2mcEuHq5YcMG4xwtwzD4T8WdoexMUcDtcuYBi4wsXR2uQM78vffeM8lC6XitZi0C2gKzlj5CNhsaH6OGD0KzCpew+Ee5qFo+zzO277o1TOX+SN9IfKVPJuZtPYAxIwfjt88+j29+69vFmjN/KfNLgis+rLjNqBr696jeUrEwWuJibYHdqwYaPdy2pXHIFU1GGslp/15OpT3CAAn69nDFh1uOdGymMR7MUPaSzJlO8vx/IuOnJPSCf48MoOAzttwIDMns1L41vjc0G4/0ptFz74qPr0nzA4ZVjns2yUavbz+Oo0cO4g9/etHX5beP079n7969Jgy1bt268u+5Tca+L2QAFaw7bn/R4XbdunWmsjy3xFSLrmBWxT3KH1D0UeQPqIoVK6J3796WDi/nZy0/79SsSUAGkDX1ErRZ0TGQNY1e+lIUxnQq+TJxTc+K0PZnYvDAS6+hRs3a+MEPf1zgnC9fvmw+rE6dOmWWpidOnKgsugWSst9BGUC+dcZViX79+plVio8++shUmadPilrJCHB7yxvKzlByuyRA5SogM9SrWZOADCBr6iUos+KHyAMjh+BHo6I9xo/v7S5/B68QF4FZT+Zh/HO/Rtt2HUxYMO/17svzVxpDUenf06dPH0s4JPorm64rmoAMoKIZecsceLfEWFRVfm5Fc/NewZVjfo4w8zsjUceOHWubnEv8HKQBxFUqNWsSkAFkTb0EZVYv/fkF1Ch7Dt8ZXHrjxzvB6okR+MOkTHzrG1/Bnv1Hcfz4cRPKHhMTY/x7uC8v/x4vLWf9lQHknz6ZJHHcuHHGOXr27NmmlpjSOxTOjqHszOHDlWNmfp8yZYrxGSz8LmudZb2v+Ph4283bWhSDOxsZQMHla5neGWn1xz/+HrO+k+WZU+AMIArYp1kk6q9NxRNPPIEHH3zQhMmzXICaswnIAPJfv4xK4koAt28WLVpknHYZAKCWn8D58+eNYzONB64ck5ldV8y4+qPyF/n1a7V3MoCsppEgzWfmzJno3jgSDavlFDrCycsVMH1DG7y2rKvnujz8YMR6TOq6FzUrphZ639d7p+P51Wvw5ptvFnqdTjqHAB1SFeFUPH1yRZRlGJKSkkxdO+bUYqFVNzduFXGlhxFdt27dMqHszLJtpVD2kuiHDtAMy1ezLoHALgVYV07Xz2zme2/igbZpRXKoX/UGfjJ6rScsPhXNa13Bk0M3FWn8sNPenlWgc2fP4OTJk0WOoQucQUArQCXTI7OrjxkzxkQxcUuMqx5ubAxlp2/PrFmzwLpyTF44bdo0k8DQ7sYP/29wFYuJQtWsS0ArQNbVTcBmxl9Ya9dtxjM/9d/eTYjJRLznX3Faz2YxJhuuoh6KQ82e1/LLi03+XSXTH7mxkjy3xJYsWWK+/JnLxmp5bEomXeF30TjwhrInJiaiZ8+ejgsVP3v2LKpVq6YV0sIfhbCflQEUdhUEfwIsNRHt0XTFeP/z/ZRkVo0qpXo+2PaX5FbdYzMCWv0JjMKYkmLChAm3t8QGDRoU1rpVgZGq4F4YhcpcYPxHH0En1/qT/0/Bz4DVjvq/JGC1mWs+fhNgREWFBE9hryC3CnF5uHLpXJBHUfdWICADKHBaYKTQ6NGjTQZpbolx9cBJjaHsTAr5/vvvm4LH3P4bNmyYowsd0wBSAkTrP8VaAbK+jko9Q26BlS2gvlepO76rg0ia056x1JxPQAZQYHXMLbGuXbuaLbFly5aZ0O9OnTrZeovx2rVrJpSdZXcYyj558mQTFh5YctbrjY7cNPq4BaZmbQJaAbK2fgIyO+6z37iVHZC+CuvkpsfHukIl/acvjJFTzskACo4mGTbNLbELFy6AGaSZvqI0jb5a/DL2+myVpi9/7+XcP/nkE3z44YcmaSFTY9DfiStdbmhc/aFvlxv8ueyuT60A2V2DfsyfH6o3UrKQkh6BhNjg+QEduxqHkc1b+DEjXWJ3AjKAgqdBFgkeOXIktm/fbmqJMVT+vvvu82vA69evgykvFi2YjW3btuPTc5dRLqYsbmVko2JiHFq3bIZBQ0fjwYceNqVp/OrUz4u8oew0uBj+PXjwYFOo1M/bHXOZ6n/ZR5UygOyjqxLPlL9EenXviA1Ht2FI65LX/ypqAhuO5OD3nurXas4nIAMouDrm/1lugXElgTmDWFKDW2S+ou4Y6PC73/wKb//7bfRrGYUBTdLw5Jcj0KBa9OcTjcbFm1k4eG63J1JzP/r97Xl07NQFzz3/11LlquHK0rFjx0wOHw7ESDaWrPA1z+BSs0bvNIA6d+5sjcloFoUSkAFUKB7nnBw76Uv46J19HgOImaALb3TjuZJSzpMG0f/Vom0nchFfviqaNm1aeOc66wgCMoBCo0ZGS7GA8IoVKzB//nwTOZWQkJBv8FWrVmHKxDEY0SYbST/OReUEpq+494cOy9ZUT4xE3+Z5+MmIXMzYtB5DB/bC93/wUzz9i1/m67OoN9T/wYMHjY8P8xp1797d71Wqovq283n6PTGHEd0O1KxPQD5A1tdRQGb48MMPY+UBT3Kum4U7KTMT9O8+6Ifk9FicvFwRLy7qgfPXi967//eGGHzz8e8FZK7qxPoEZACFTkfMFM3q51xZmTNnDuhU7G1v/PN/MWHMcLzyUBp+OSbHY/z496OlbGQEHuoZiY+fysELf/gthg8b5JefUEZGhmdrbRveffddU+iToewPPPCAjJ/PFaLtL++TaY+/EZ4IocK/Ee0hh2bpB4Gf/vgpnFj3Tzw3KbAO0Xs+zcU334nDoaOnwF+Das4nwAy+dPYcOHCg84W1kITc6mJl+QYNGiA5ORnDhw/H0p9Fo1H1kv+WzcjKw8OvA806j8B7M+YWKC2dsVmVnas+HJtbXRUrVizwWjcfZJ23Zs2aGWPVzRzsInvJ/9fYRULN8zaBn//if7DxVDw+2fVZFt/bJ0rxIiXdUy9sejSef/FlGT+l4Gi3W7UCFB6NsYo8t8S4CvPglHGY9b3SGT+UIiYqAv/5FrBvy1I8+7vf5hOMTtUrV640jtU8wVB2OmXL+MmHybyhP9S5c+eM39a9Z3XEigTkA2RFrQRpThUqVMBHi5LQr3c3VIzLRvcmpbN/M7Pz0O7nGfjxD74NbrGpuYeADKDw6vrlv/wJf5iUg04N7vX1KcnMaAS98dUsjHrhOYwc9YD5EmdxUtYpY40uhrLHxMSUpGvX3MPwf37Gur24rZ0UXrpvQDtJqrkaAq1bt8bipFV48JVMzNpU8q2wZM/Kz9RXIzFm1DD88YWXRNdlBGQAhU/hb775JmrHXceQNoExfrySVPH4D313cBa++51vmG02RqA99NBDJhpNxo+Xku+/8v/xzcaqZ7QCZFXNBHFeDNHcuXMnRt4/CMeupOL7Q/MQU9Y/50lOi+Hu/zUnBl/66nfwzLN/COJM1bVVCdAAiooKfnkVq8ofznm99Pyz+N2odM8U7v39mpoRhXfWtMcLH/fyxHDmYVK3fSgbmWsStN9Mi8GaQ/Xx3+NWYFzngwWKMLV7Gfzlmb3o0qVLwPMEFTiggw7SJ46pCtTsQ0AGkH10FdCZtmvXDlt37MVT33sc/X+/CI/0zsH4ThGoWbFgQyjDs921cn8u3t1cDqdulMNLr76GcePGBXRO6sw+BLKyssCEfWqhJcBtqay0656tr3uNH84kPiYL3x68BR/vbIrLKXF4dkpSvgku3dMIV1PL5Tt25xtGh43rXAbT338P//3LX915Sq8LIZCZmQnWXGTaAjX7EJABZB9dBXymNWrUwLvT55jqzH996Xk88LdZqJIQiaY1I1C5XJZnVciTRj8zCsevRGLn0WR07dQWj/3kKUydOhXR0d4EawGfljq0AQFtgYVHScwH1LtJ0UEMcTGeXEAeA+ju1rf5SWTnFmw8ea/t2zQLb3wyXwaQF4gff7n6Q+OHOYDU7ENABpB9dBW0mdIv6PX/fROvvv4G9uzZgwMHDoDhtvyVz4RezELLzK7c8mBNHzURkAEUnmdg1/aNaF49wzN48T+6L96Mw+zNrfC4Z4WosNasZhnsm3m4sEt07i4CNIBU/f0uKDZ4W/z/RTYQSlMsGQEaOdwa47+7Gw0ihsPKALqbjDvfywAKj97PnTmNvo0K3qa+e0b0B3pvfRtzOCU9Gkv3NPaUyThx92X3vK9RIQJXr6d4/IY8ueAj/Bvrnk5cdoAO0EOHDnWZ1PYXVwaQ/XUYEgmqVatmKlMzIZpbqjqHBKxNB5EBFB7FpaWnIdZP3/PIMnmoUSHFTDQhNhqVEtL8nnS8ZxD+X7+77IbfHbjoQhZ/pQ9Q5cqVXSS1M0SVAeQMPQZdCv4SZEXq06dPo0ULVXwPOnCLDyADKDwKSkgojxTugPnRYqOyMajVidtX9ml2ypP6otXt975e5ObmIc1TPV5O7r4I5T/uDX/Xall+LnZ4V7g3nB0k0BxDRsBrAIVsQA1kWQIygMKjmqbNWuPEpZJVL6oUn47HBm4rcuInr+ShXp1qrq7oXiSkOy6gAVSnTp07juilXQjIALKLpiwwTzr50dmPKd/V3E1ABlB49N+5W09sP3NvdFdxZvPGyo6gQ7Svtu1Enif5YWdfp3X8DgL0k5ID9B1AbPZSBpDNFBbO6XJJnMVO6RCt5m4CMoDCo3862q4/lAmWoSmsXfPk+snMjjQJEO+8bvfp6pi3pSWqJ96683C+1ysOl8OIBybmO6Y3BRO4fPkyypUrJ7/IgvFY/qh8gCyvImtN0LsNpoRf1tJLqGcjAyjUxD8bj7m7unXtgo92bMT4LvfmnGHk15ueFZ7DF6p4jJ8IPPzqRFSOT0OGxxg6f7089p6pZrJD+5r9peQ8rDmYhf94Cq6qFU2Aqz/a/iqak1WvkAFkVc1YdF7cBtuwYYNSvltUP6GalgygUJG+d5xHvv44fv6DbXigY47rmhVIAABAAElEQVSnzEX+MHVmgn5y2Cbz7947iz7y92Vl8bWvfdUU9Sz6al1B/5+2bdsKhE0JaAvMpooL17S58nPjxg2kp7MWkZobCdDvISfH8+VbVr+fQql/brd8+OGHhnvrDj3w0pJ7V4BKM5/Nx3KxeF8Ufv4LlcDwhyN/BNAdgEVj1exJQJ9g9tRb2GbNZIlc8mU4fNOmTcM2Dw0cPgI0fpTyP3T8b926hc2bN+PUqVOmSCnTUAwbNgxdOrZGrcRLeLhX6Q2hw+dzMfXlTKxenYSqVauGTjgbj3T+/HnDSkWB7atEGUD21V3YZs5tMBlAYcMf9oG1/RUaFdDQ3LVrl/lHo+fOGnwVK1bEoiUrzY+QvWfy8Ozkkn+U7zuTi/F/ycac2bPQp0+f0AjngFEU/m5/JWoLzP46DLkEdITmf35uhai5j4AMoODr/NixY5g+fTouXbqE8ePHo3v37vcUIGaNPlYg33yuOp5fVAZZOcX//7hoVw6+8s+yeOf/3sX4CXJ8Lo5m+Rmo+l/FIWa9a0v+s8F6smhGISLAUPjY2FjQJ4ElMtTcRUAGUPD0TYNn3bp1IOOBAweiVq1ahQ5WqVIlbNm+F498aSqGvbAOTw7MwOiOZRBTNr9z9N2d0N/n7yticPpGBXywQCs/d/Mp6n1aWhqSk5P1+VcUKIuflwFkcQVZdXrebTAZQFbVUPDmlZWVBfk9BJYv625t2rTJrKx27doVzZs397sQKbfD5n24CKtWrcJzz/wSz/xmE3o2i0bHOrfQoFoE4mOA9Czg7LU8HLoUg9WHyiCufGU89eOn0a1bNxw/flyFT4upToa/0/mZPpFq9iUgA8i+ugvrzOvVq4etW7d6MsZ2Cus8NHjoCWgFKHDMyZJ+Prt370bLli0xbdq0EhmXrEPVv39/z7+VJjJp6dKl2LJpPT46tA/JKTdNsr5ateuha79u+IlnZckbus1tbBpABw4cMOMHTjJn96TtL2foVwaQM/QYcikYDn/lyhVTBTk6Ojrk42vA8BGQARQY9keOHMHGjRvB5IYTJkwwWdYD0XP16tXx0EMPmX9F9UfDqV+/fliwYAEaNGhgDKWi7tF5mJW6jh07CoXNCcgAsrkCwzV95oChfwJ/CTVq1Chc09C4YSAgA6h00Jk7hn4+rKk3ePBghDureuXKldGsWTOT4JR+R2qFE7h+/brZ+qpQoULhF+qs5QloA9PyKrLuBL1lMaw7Q80sGARkAJWMakpKCpYtW4bFixejVatWJror3MaPV5IuXbrg7Nmz5p/3mP4WTIA/+lT+omA2djsqA8huGrPQfL2O0BaakqYSAgIygIoHmby2bNmC2bNnIzEx0fj5cMWF209WaVzRZQ4gOlIz/5CabwKq/+Wbjd3OyACym8YsNF9GnzAKgrlI1NxDQAaQf7qmg/GhQ4fw/vvv4+bNm5joKTDKlRarlhCpX78+uB22Y8cO/wR04VXctuRKmfL/OEP58gFyhh7DJgWjwZgVmh+cau4gIAOoaD2zTAL9fPgDgWUr6Jhsh9arVy+zUsUki/JxuVdj9N/iKh7zoKnZn4BWgOyvw7BKoG2wsOIPy+AygHxjZ3I8hqAnJSWhXbt2GDdunG2MH0qVkJAARjetWbPGt5AuPqPwd2cpXwaQs/QZcmnoDMhfRfxSVHMHARlA9+qZySGZyJB+PlwNZd0urqLYsbVp0wbMdMwwfbX8BOT/k5+H3d/JALK7BsM8f2YE5vI+PxjU3EFABtAXeqafD5MI0s+HVdunTJlikoNa1c/ni5n7fsVtO+YGWr9+vcnz5ftKd53JzMw0uc+sErnnLvrBkVY+QMHh6qpevdtgdKJUcz4BGUCf6ZjOsDQSaOwMHz7cUXWh+KOGiRG5qqUK8V/om0kr7WzcOv/TqXgSagWoeLx0dQEEvNXhCzilQw4k4HYDiBFdzOWzYsUK4y8zduxYRxk/3keWFehZJoNb3Gowq9zK/+OsJ0EGkLP0GRZpqlSpAvpA8ItBzfkE3GoAcQtkw4YNmDt3rjF46Ofj5CzoLHHTs2dPkxuI4d9ub3KAdt4TIAPIeToNi0RcBTp16lRYxtagoSXgNgOIfj779+83fj4ZGRmYPHmyWfmJjIwMLfgwjEZHboZ879mzJwyjW2dIZvFOT08Hf+ypOYeADCDn6DKskmgbLKz4Qzq4mwwgOvfPmjXLRESNGjXKVFyPi4sLKe9wD9a3b19s374dNALc2rzRX1bK3u1WXQRSbjlBB5Kmi/uiI7Q3jb4bfhm7WNUm5YHTHUFv3LhhHJyvXbuGHj16oGHDhq5VORMiMjSeiR2Z1NGNTdtfztS6VoCcqdeQSxUTE2Pyn5w7dy7kY2vA0BJw8goQt7gY2TVv3jzUqlXLhLW72fjxPlkdOnQwJW9OnjzpPeSav9wC5QqQyl84T+UygJyn07BJpOrwYUMf0oGdaADRyXfv3r2YPn26WeFiPp/27dtDq5mfPVrkwNxAzBBN/bupXblyBfyBxyzZas4iIAPIWfoMqzTefEBhnYQGDzoBpxlArGVHPx+GfI8ePRr0eSlXrlzQOdptgNq1a5tVMVa2d1Pz+v+4SWa3yCofILdoOgRyVqtWzaTQp7Okfi2FAHiYhnCKAXT9+nWz3UV/H4Z7K5Fn0Q8UOc2cORPNmjVzTQFkGkCtWrUqGo6usB0BrQDZTmXWnTAjJLQNZl39BGJmOTk5oJ5ZLsGujeHMa9euxfz5841fB7e7ZPz4p02ujHXt2tUEPNA3xumNz/v58+fB1S815xGw76eY83ThCIm0DeYINfoUws6rP/TzYT6bGTNmgF/eNHzatm1ra2POp6KCeKJFixamd9ZAc3qj8cPitkwKqeY8AtoCc55OwyoRV4D465pfNnZeJQgrRAsPblcDiEk6Gd1Vvnx5PPDAA6hUqZKFKVt7alwBpEP0ggUL0MBTL8zJ/lIKf7f2s1ja2ckAKi1B3Z+PAD8MmTfkwoULxmEy30m9sT0BuxlAV69eNYZPamqq8fOpV6+e7XVgBQG4KkI/IBqVgwYNssKUgjIH+v/06tUrKH2r0/AT0BZY+HXguBloG8xxKr0tkF0MIPr5rF692qxS0L9n0qRJkPFzW40BedGlSxcw79fZs2cD0p/VOuEzREf56tWrW21qmk+ACMgAChBIdfMFAZXF+IKF015Z3QCi0+quXbtMPh/mrmHBUmYx1nZs4J9EZgPv06fP7QzwgR8hvD1y9YfOz3p2wquHYI6uLbBg0nVp3zVq1ABDi9PS0hztH+BG9VrZADpx4oSp1l6xYkWMHTsW/KsWXAJcXTt48CB27NiBzp07B3ewEPdOA0jZn0MMPcTDaQUoxMDdMBx/MdWpUwdMMKfmLAJWNICYqZcOuZs3bzYrEsOHD5fxE8LHjj4yjK7jjx4nNTlAO0mbBcsiA6hgLjpaSgLaBislQIvebiUD6NatW2b75aOPPkKjRo0wceJE/WIPw3PDpKcdO3Y0ZTLCMHxQhqQxx0hWrSIGBa9lOpUBZBlVOGsi3oSIbkiW5izNFS6NFQwg+vlwy4UZiZmfZdq0aSZTr3w1CtddMM/Sz4pb3keOHAnmMCHrW6s/IUMd1oHkAxRW/M4dnL8KGRJ/6dIlRVE4SM3hNoCOHTtm/HyqVq2K8ePHIzEx0UF07SsKjU/mBvrkk09MtJ3dEwfS/6dhw4b2VYhm7hcBGUB+YdJFJSHg3QZTGGlJ6FnznnAZQJcvX8a6deuQmZmJAQMGqDSBBR8P/j+n0bBx40ZTUNaCU/RrStz6Ymg/i+KqOZuAtsCcrd+wSkcDiBl41ZxDINQGEP18VqxYgY8//tgk3qOfj+oyWfd56tatGxiNd/HiRetOsoiZcdXau4JdxKU6bXMCMoBsrkArT79WrVpgJt6MjAwrT1NzKwaBUBlAHGfbtm3GzycuLs7k82ENKpZhULMuAW59sWL8qlWrjBOxdWfqe2YKf/fNxmlnZAA5TaMWkoeJ6PhrnQ6Fas4gEAoDiI6006dPN8bzhAkTwFUFu/uUOEP7/knRpEkT4//H0Hg7Nn5eMY2HmvMJyAfI+ToOq4TeshiNGzcO6zw0eGAIBNMA4rYJ/XzogzF48GDUrFkzMJNWLyEnwAzR8+bNM+kJuJ1kl5aVlQX6m3H1Ws35BGQAOV/HYZWQfkAMWVazHwF+GSxZsgTLk5Zg987NuOgpcHvj5k0keiqqN23WHF169MOIESPRtm3bUgnHQqV0nKXjadeuXY2vj7a6SoU07DezIDJD42nQDhs2LOzz8XcCrG1GZ26W+VBzPoEIT56WPOeLKQnDSeC9997D/fffD1aQVrM+ARaA/PMLf8Jrr7yMJjUj0athKprXyEOtihEoFw0kpwMnL+dh2+korDhQBhWr1MTPf/mMKTjKbU9/G1eTaBxzq6R169bo0KEDoqKi/L1d11mcAPM1MVcTfYJYMsMOjQYb03cwsaOa8wnIAHK+jsMu4Zo1a0xUBb/g1KxNYOHChfj6ow9jQLNsfHtAJupVKdpNcPXBHLyUVA7lqjTGzDkLiozS4m8u+vls2rTJbHN1797dPB/WJqPZlYQAV/WWL19unNjtsKoyY8YMDBw4ENWqVSuJuLrHZgS0zmczhdlxutwG2717t/mFb8f5u2XOf/rDc3jhj7/Fq1/ORof6NHyKNn7Ipm/zSPRploG31uxHs6aNsP/AYVDnBbULnm00/spmGzJkCFg4V825BBgEwX9btmxBjx49LC0ot2KZzZpJNtXcQcC/Tzh3sJCUQSLAD0A6uNKnRM2aBGa8/x8897tfYvYTOZ8bP8WbJ312vurJG/fshFyTCZgh7He2lJQUJCUlYenSpcY3ZNy4cTJ+7gTk4Nc0fA4dOmSi+qwsJsPf+Vkl/zMraymwc5MBFFie6q0AAvTroGMhP2DUrEdgzpw5mPrgl7Dwh2VQu1Lp8uw80CkSL38lCg+MHIrz588bo5dV2mfPnm0KS06dOhVNmzbVl4z1HoOgzYg+NXRuZ24gK7ucqv5X0B4By3YsA8iyqnHWxLglonxA1tPpyZMn8a1vPIIlP41G9cTSGT9e6UZ2iMSXu93CtMlj8e6774KrP5MmTULnzp0VXeOF5LK/TGLJduDAActKLgPIsqoJ2sRkAAUNrTq+kwANoNOnT995SK8tQOCXT/8UD3XPRuMaRX8UZOdEeKK/KiAl/YtIreupMQVK8c0BeTh7fC9iYmKMU2l8fHyB1+mgOwhwW4nFUrkaSD8bqzVmrGeyzfKeFA9q7iEgJ2j36DqskjIEnmGxN27cAHOEqIWfAHOezJ//AVb9FzNh+F79WX+4Lv74UR/UqXQTzWpewdXUcth/5rMomQGtjuPxwVvuEYZfeN8fnInXXn4B06ZNu+e8DriPAD8DmjVrhvXr12PQoEGWAqDsz5ZSR8gmIwMoZKg1kHcVSAaQNZ6F999/H/e3K4uE2GyfE3p5STe8srQrXv/ah55or1O3r+Mq0HfeGo1bmV+sBt0++fmLYW3L4L/n7TG+XyotcDcdd77v0qULGGpOf0ArPRM0gFq2bOlOpbhY6qLXvV0MR6IHloDXAApsr+qtpASWfDwX/Zr4LlS7+3R1vLSoB77Ue1c+44fjJcRm4TcTlyEz23fiw7KREejTPNpEf5V0jrrPWQSYC6h3795YvXq1WRG2gnRcmabDPiPA1NxFQAaQu/QdVmlZF4zbLswArBZ+Att37Ea7er4/At5a3QG5eWUwtXvBRS0bVLuBJ4ZsLlSQNjVvYdvmDYVeo5PuIsCs0NwOs0qJHOamqlSpkvFXc5cmJK3vTz+xEYEAE6CTIZOM0QhSCy8BGqFXriV7/Hp8+/4cOV/FTLJu5Zs+J5tYzvcKEm9qUC0CJ44d8nm/TriTQK9evUwJFPoEhrsp+ivcGgjf+DKAwsfelSN7q8O7UngLCc3Q9Phyvv13ONWLyfGIjcpCTFROiWfO2mEpqcklvl83OpMAK8Sz3ha3wsLdrOaPFG4ebhpfBpCbtG0BWZUPyAJK8EyBX0CpaZmFTqZe5RtIz4qCr1D3Qm/+/CSHiI9TCLw/rNx2DavFp6enm7pw4ZI9IyMD165dMzXpwjUHjRs+AjKAwsfelSNzC4wfelyBUAsfATqjVkiMx4UbDIEvuLWofdmc2H+25IUhT1/JQ936jQseQEddTaBMmTImNxDD4mmIhKNx9adWrVrgXNTcR0Bad5/Owyox88NwG+zUqVNhnYcGB9q1boF9Z3J9ovjGgK2IjszGq0ldfV5zM82zx1VI23+xHNp39H1/IbfqlAsIsEROw4YNsWnTprBIq+2vsGC3zKAygCyjCvdMRNtg1tD1kOHjsOqIbwPmvio38cOR67DmUD0TDp93x2JRrsduem99Gyzf19CnMKz7tOZgjqn67vMinXA9gW7duuHEiROmYHKoYcgBOtTErTWeDCBr6cMVs6EBxF9eufwWVQsbgWkPPogF23ORkX2HZXPXbB4buA1vPjYP87a2wOgXHsJv5vbH998Zjgf/PhnVElMxtvPBu+744u3KA7m4r159NGrU6IuDeiUCdxFgdGjPnj1NsdRQfibcvHnTpORgSL6aOwkoE7Q79R5WqWNjY005DCUfC6sazNZDv/798c7a5fhGf99z6d/yJFY8/RZu3IrBsYuVPIbPLVMWw7ObWWh7eXk5/PR3/1PoNTopAiTQpEkTHDx40ITGt2vXLiRQtPoTEsyWHkQrQJZWj3Mnp20wa+j2md+/gNeWR+Dcdd+rQN6ZVojLQMcG58G8QEUZP9M35iIysR4mTpzovV1/RaBQAn369MH27dtDFiAh/59C1eGKkzKAXKFm6wlJA0jV4cOrF4b/HjhwAN98/Ht45I2yuJlWtBHkz4wXbM/B84uj8d6MeYiM9F0qw5++dI17CLBGYNu2bbF27dqgC03/NBpADMhQcy8BGUDu1X1YJWf0R3JyMm7duhXWebhxcH7479mzBx9++KEpAPn75/6AoaOnYfxfUWojaPOxXHzvnSysWLUejRsr/N2Nz1dpZG7fvr3Jy3Py5MnSdFPkvZcuXUJ8fDzi4uKKvFYXOJeADCDn6tbSkjHvBqtBcx9eLXQEUlNTsXDhQpN8bty4cbcrYL/y2j8xetJX0eeZHJy+UjLn9I935uLxd8rik08+AZPcqYlAcQlwxbBfv35Ys2YNsrKyinu739dr+8tvVI6+UAaQo9VrbeG0DRZa/Rw9ehSzZ882id/GjBmDxMTEfBP468uv4qW/vorxL5fFXxYDyen+bYkdOu8xfP4vCi+uqo4167dg2LBh+frVGxEoDgFWZee/rVu3Fue2Yl0rB+hi4XLsxTKAHKta6wvmdYTmloxa8Agwy+6yZcuwZcsWjBw5Ep06dfKZ+fbr3/gGtu/aj5s1JqDvsxF4ano0Zm/Owe7TubicnIfUjDxcvJmHTUdz8UpSDqa8FoNH34zDkGm/wM49B2+vKAVPGvXsBgI9evTAoUOHcOXKlYCLy0LA3AJjBmg1dxOI8Hz56NvH3c9AWKWfOXMm+ntCsekTpBZ4AlzqX7FiBRo0aIDu3buDJTD8bfzymTt3LpYtXoDdu3fi4qWrSEvP9NT2ikWjhvehS7feiIqJxy9+8QtUrFjR3251nQj4RWD//v0mNH7s2LGeqMMici741eNnFzH4gtFmXAVVczcBGUDu1n/Ypd+wYQOioqLQuXPnsM/FSRPgr1yWFzh27BgGDBgQtGgXGldVqlQx0TtO4idZwk+Av80/+OADNG/ePKAri6w9FhMTY1ZCwy+lZhBOAtoCCyd9jQ35AQX+Ibh8+bJZuWGE3eTJk4Nm/HDmzPJ8/PjxwAuhHl1PgKs+dIjevHkz0tLSAsZD4e8BQ2n7jmQA2V6F9hagZs2aJuyVFeLVSkeAv5h37Nhhorw6dOhganDxl24wGyP5rl69qnQGwYTs4r5ZpoIrQFy1CUTjj4KUlBRUq1YtEN2pD5sTkAFkcwXaffoMe6UzosLhS6dJ1jWaP3++4ThhwgQ0bdq0dB36eTf1V79+fa0C+clLlxWfALfHWTaHKzelbeyDEWaB9Ckq7Zx0f/gIyAAKH3uN/DkBbzSYgJSMAGsozZs3z9T2GjVqFBISEkrWUQnvatiwoQygErLTbUUToOM+y2SsXr0aOTk5Rd9QyBXa/ioEjgtPyQByodKtJjINoFOnTlltWpafD7cNFy9e7InQ2o3Ro0eDRSTD8cuW5QQYVqxtTMs/MradYL169cDtMG7xlqYp/09p6DnvXhlAztOp7SRiQr7o6GjQeVfNPwI0GJlCgPWTuOXFL4dwNf5C5xeUnKHDpQF3jNu7d29TwuXGjRslEpi175iB/u4EoCXqTDc5goAMIEeo0f5CaBvMPx2yPMCqVatMqYChQ4ea3D78UA934zYYQ+7VRCBYBFi7q2PHjmYrrCRjaPWnJNScfU/4PzmdzVfS+UlA4fBFg7p48aIpZZGbm4tJkyaBEXRWaVwB4vy0DWYVjThzHqwxx2fsyJEjxRZQ/j/FRub4G2QAOV7F9hCQkRn0IwlmAUR7kLh3ljR4WMZi0aJFYIkAJjbklqGVGrfB6AsU7CreVpJZcwk9Aa52MjcQw+JZ4sXfxv9DZ8+eNRFg/t6j65xPQAaQ83VsCwn5BVqjRo2AhLraQmA/J3n9+nUT4UXjkKs+LGlh1aZtMKtqxlnzYtkcPmvMdO6r0ThiKRdvAsULFy6Yci2xsbG+btFxFxKQAeRCpVtVZG2DfaEZJjXcs2ePKQXQokULjBgxAnFxcV9cYMFXzAd07tw5ZGZmWnB2mpKTCHTr1g0nTpww266Ui6s7L7zwPIYP6YPqVSqgQmI8mje+D1UqJaJyhXiMGzUYSUuXlGjrzEncJEt+Av5XRsx/n96JQMAJ0ADil77bG7PVssYWDYlx48aZSC87MGFNN2aG5jZYqBIx2oGL5hh4AtwC7tmzJ2bNmoUNa1fgww8/xMj2ZTGhWQZ+1bsMalaM8gzKOt9lcTk5B/vPHsGqgyfRq+vb6NGrN37/x5fQunXrwE9MPdqKgFaAbKUuZ0+2UqVK4MoHt33c2hhJxQ91bgeyWjXD3O3UtA1mJ23Ze67MCfTfP/8xKl/7EKt/nodnJmRjaJtIj/GTv3J81fIR6Ns8Ek+PzsWap3PRKWYV+vfuhr/99S/2BqDZl5qAVoBKjVAdBJKAdxusYsWKgezW8n1xtWfNmjVmSZ/bXXatVUQfpbVr1xpndq4IqYlAMAj8z69+gX+++iJmPJ6NRtX5Oz6/0eNrzKjICDzSNwIj2ueg91NPYfOm9Xj7nffCkkDU1xx1PHQEtAIUOtYayQ8CXgPIj0sdcwn9F5jUkMv6dHS2q/FDhVAGhucrGswxj6flBHnn7Tfx5z89h9lP5Hxu/BR/itUTI7D9mWhsXDEPTzz+WPE70B2OICADyBFqdI4Q9CFh4cPs7GznCOVDEtY12rBhA5YtW4b+/fubekeMhrN74zaYskLbXYvWnP/ChQvxvSe/jYU/KgsaMaVpCbERmPkEsGTB+3jt1VdK05XutSkBGUA2VZxTp80VhKpVq5qoDqfKSLkYojtnzhwkJyebVR/m0HFK4zYYs+66wYh1is7sIAd9Ax/72pfwr68DdSqVzvjxylveYwS9+dVsPP1fPzZRZd7j+usOAjKA3KFnW0nJbTB+gTqx0cmbzpsfffQR2rdvD5azcFpuEsrDXC0qcOvEJzh8Mr345+fRt0kWOtQP7NdWncoReLR3Dn759E/DJ5xGDguBCM8HMmMF1UTAMgRYFDUpKQlTp061zJwCMRGu9ixfvtw4XA4cOBAJCQmB6NaSfezfv9+s4g0ePNiS89Ok7EWAGeLr1KyKmY+no16V/AZQVk4ZvLOmPV5c1BOpGdEY2+kAypfLQHxMJtKzyqJ5zSsY13k/YqNzfAqdmpGHPs9G4PDRU7b2wfMpoE4USCD/k1TgJTooAqElUKVKFZPmngaDU9qhQ4cwd+5ck8l59OjRjjZ+qDNug3EFiH5OaiJQWgL0k2O0193GD/uNiszF1/pvR+eGZxEblYUXv7QIv5m4HD8dvRaP9t2Bj3c1wbS/T8alm74TicbHRGBIm2izLV3auep++xCQAWQfXblmphEREXBKNBgLNy5evBi7du0CDZ927dq5IuS2XLlyxpfr9OnTrnluJWjwCCxftgS9Gt4qdID46CzP/638l9SvegN/f+Qj3EyLwfiXpoGrRb5ar4ZpSPrkQ1+nddyBBHw/DQ4UViLZh4ATDCCugDCpYWJiIsaPH4/KlSvbRwEBmGmjRo0UDRYAjuoC2LV9k2crq2TeGgmxWXi03w6cvZ6IPaer+8TZvFYE9u7Z7fO8TjiPgAwg5+nUERIxKor5cVjF2W6N0U9Mash/9IFhBffIyEi7iVHq+TIcnvmA7KjDUguvDgJKgKkxat2V4bk4AzSvedlcvvlYHZ+3sf/zl674PK8TziMgA8h5OnWERIwkYjZofvDZqV28eNGs+tBpk0kNa9WqZafpB3SuLN7K8iZOjegLKCx1ViiB9LR0lIsu9JJCT1ZOSDPnz1xL9HldrCdxeUZGls/zOuE8AjKAnKdTx0hkp20wrnJs2bIFixYtAitVM8qLOY3c3rQN5vYnIDDyly+fgJT0kvd1+HwVc3OL2pd8dpKSAcTHxfo8rxPOIyADyHk6dYxEdjGAmKDtgw8+MHW8uOrDL321zwhwG+zEiRPaBtMDUSoCjZs0xfFLJfMB4sD7z1Y147evd8HnPE54+m/U8D6f53XCeQRkADlPp46RiDWxUlJScOtW4dEf4RR43759xvhp3rw5Ro4cCW77qH1BgLmOWNGe/lxqIlBSAl169Me20yVbUT16oRL+vaYDpnbfg1Z1fK8AbT2Rhy7d+pR0irrPhgRkANlQaW6ZcpkyZUBnaCuGUtMoY12igwcPYty4cWjVqpVb1FJsOVUbrNjIdMNdBEaMGIEVB4DC8vZeSS2HnNwIzzVf3LzbE/X12BtjPPmDbuBX45d/caKAVyuPxGPEqDEFnNEhpxKwf+VFp2pGchkC3m0wrrBYpR07dsxEeNHo6dSpE2ioqfkmQANo3rx5ptgrczypiUBxCbRs2RLVa9bFiv3HMLBV/ohK5vb518qO2HmqJjKzy+LRf4xDoicTdJmIPGTnROIrfXbi4d67TMJEX+MePp+Lk54AMJamUXMPARlA7tG1LSWlAbR+/Xrzyy/cX56ZmZlYu3YtLly4gOHDh5t6V7aEGuJJMw8St8LOnTuH2rVrh3h0DecUAo89/n289MefYEBLJjz8wpBmJuhvDdpq/pVU1heXxuAHP/oZoqI8oWBqriGgn66uUbU9BaVPDb88GV4ezsYvbyY15AckHZ1Z7FPNfwJcBeLKmZoIFJfAtWvXzHYzUyok1miGN1Z9YfwUt6+Crl+4IxdHr1fEE09+t6DTOuZgAloBcrBynSKadxusRo0aIReJtaw2b96MI0eOoF+/fqhXr17I5+CEARkZN3/+fPTu3Tvfr3cnyCYZgkMgIyPDpJY4evSo2WrmlvOAAQPQuUNr1ExMxuiO+bfCSjKLjUdy8eS/M7Fv3ycKYCgJQJvfoxUgmyvQDdP3GkChlvXq1aumgOnNmzfNqo+Mn5JrgJFgrA/G7UM1ESiMAHNq7d27F9OnTzdb31OmTEGbNm2Mr13NmjWx8JNl+N47nqKni0qXtHDd4Rw8+EqmMbLoY6TmPgJaAXKfzm0nMT/0mGuHhUWZITrYjZEmu3fvxo4dO0wZi2bNmgV7SFf0790Goz7VRKAgAmfOnMG6deuMscziwQXVz2vfvr1Jq9C9S3tER6Xg2wNzEVmmeNti72/IxZ+XxCIpaRE6d+5c0FR0zAUEtALkAiXbXURGWdF5NhQlFZh3aMGCBSZ5HwuYyvgJ3NOjrNCBY+m0nrjK+sknn2DVqlXo2rUrfBk/XrlZYmbrjr3Yk9ENI1+KxgdbczyV3u+If/deeMff3Nw8LNuXg4mveK4/2gIbNm/HoEGD7rhCL91GIMLza7fwp8ZtRCSvJQkw4SC3T1hiIljt0KFD2LBhA9q1awf+yrwz0iRYY7qt3xkzZhg/DjmRu03zBcvLyMrt27fjwIED5v9c27Zti1U4mNtlSUlJ+OOzv8KWrdvRs3kM2tZIRf2qEUjwLBbf8pS3OHUlD4cux2H1gWw08Djj/+hnv8KECROKNU7Bs9dRuxOQAWR3Dbpk/snJycYf58tf/rIxTFJTUz2FCzNMhFhpa25xa2316tVmm42/CKtU+axukEvQhlRMOpTTsbxHjx4hHVeDWYsAf3fzBwefByY7Zf280mZRZ+Hk5cuXY8umDf+/vTsBj7I6Fzj+hoTshCUYSASCIQgIgRAMqwgiRRAteC0CIi6t7XXp7W2rtdX2tnrb3qft7ebtom21dakIbiAoRVCEKrJJ2JE9EAlbiCyBkJCQ3HlPG6jCmUySmcnMmf/3PBE48833fed3RublLO+RPbu2erLIl0lSUrJ0zuwm/fLyzT+edBiWA4E6AQKgOgl+DWmBgoIC+dEPH5PCHVtkx+4is7dUQlyMnKqoltS2rSTfM45/w02T5eabbzY7kPtaGR1WW7JkiWRnZ5uu9+jopq8s8fXekXheaWmpLFy4UKZOnRqJ1afOHgENVHSejw5t66pA3fKGA4HmECAAag517umzgCYe/PYDX5V9e3fKTf2rZFh2rfRI1+7t85Mei4/Wyoe7a2Tx9nj5u6eb+64775Lvfv8xad/+HxsgXuxm1dXVZrhr79695l+GJOi7mFJgymbOnCmjR4/22j6BuTNXbU4BnV+3cuVKEwANGjTI/KOjOZ+HeyNAAMRnICQFdJjkgW/8h7w041n5zrgquTHPt56ZI2W18tvFMbLoo1h5dc6bMmTIkAvqV1JSYuYN6DyUq666Spo6hHbBDSjwKqBfgjq/Soc9ONwX0H9srF+/3qys1OXsubm5EhPDAmT3Wz70a0gAFPptFHFPWFVVJePHjpKKQ+vk97dVSVLc+d4eXzFW7Dwrt/6+SubNm+tZUXKjeZtOmNSl7Zs2bTKBj65K4gi+gAagixcvlsmTJwf/5twxqAKaxFAXFmgSU533pVndORAIFQHC8FBpCZ7DCGiQ0qN7lvRoUyJ/vksanN+jjnFwdrTMfzBKJtw0UV5+5TWz8kgnSNZtZdHUCZd19+HXhgvonA/t4dNEkxfL89LwK/KOUBM4cuSI2TdPe3+uvfZaIfdTqLUQz6MCBEB8DkJK4Otfu1+k4rD8ZnpUo4Ofugr1zGghL301Rr54x63yjQcflokTJ5qMsnWv82vzCWjvm+4NRgDUfG0QiDuXl5eblV1FRUVmUUGPHj1IJxEIaK7pFwGGwPzCyEX8IbB06VKZNul6WfjAWUmIbfiwl+0Z5niSpD27rqusWf8RuT9sSEEu15xOmvRu0qRJQb4ztwuEgPboafZ0neujQU9eXh5z6wIBzTX9KkAmaL9ycrGmCDz0zfvlkeurfAp+zlS38CQ5860Dc+KAaImrPiSahI8jNAR0ArrmcdItTjjCW2DPnj3m/y0NajV7us71YWFBeLdppDw9AVCktHSI11MnSpYeKpJx/Xxb7fXa6ivk6aV5Ptfq3qtPy69+9iOfz+fEwAroKrC6YbDA3omrB0pA53C9+eabsmrVKhk+fLhcd911kpKSEqjbcV0E/C5AAOR3Ui7YGIGZLzwnE3M9eet9PJ5f1lde+KCvZ/8f3z7CV/ds4dnfq1AKCwt9vAOnBVqgbnPUQN+H6/tXQDOnv//++2bPvMzMTPnCF75gsjn79y5cDYHAC/j27RH45+AOES7w7jtvyVXdfUNYU5guw3sUyeETSfLWhmyf3qQ9DsN7tTSp8n16AycFXEBXBp0+fVp0I0yO0BfQFZqaQkKHkvX/p1tuucUsKtCMzhwIhKMAn9xwbDXHnln3Bdq2q0h6ejI8+3LM/rCXfG3MChmSvU+eeS/Xl7eYc7qnlsuWTet9Pp8TAyugX6L0AgXW2F9X1y1jXnnlFdHM6bpTu25hER/v2W2UA4EwFiAACuPGc+XRdS5BQpxnonLL+gOgI2UJEhNdI4lx1TJ1yEYp2JMhm/b5tpdQWkqU7N+3xxU2J+pBABTazai9cwsWLDBDXpq5e/z48aQuCO0m4+kaIEAA1AAsTg2MgA6DJMb7tqJr1so+cnP+FvMgY3J2SrukcnnWx16gxFhPiiHPvThCRyA9PV3Kyso8O3efDJ2H4knkzJkzJoPz7NmzRdtI0xV07doVGQScEvDtW8epKlOZUBPQlSNl5dWex/LeA3S2JkrmrulpznpvW6apRsc2J2Xe2h7y8I3vSbvkCq9VO+mZY92qdRuv5/BicAV0/khdL1Dfvn2De3PudoGAGY7ets0kM+zSpYsJfMiafgETBY4I0APkSEOGczU0AIqNbSm6kam3Y/GWy+RzfXZJflbxuZ+vXLNGzlTHyMwVOd7eal7bU+pZep19Rb3ncUJwBeoCoODelbt9VuDgwYOiPT7bPAHQuHHjZMSIEULw81kl/uySAAGQS60ZxnXJ69fHM5+nxmsNZi7PkS+OKPAEP/vP/Xw+b5v0vvSwWRKvPUTejrX7EiWfHci9ETXLa5deeqlJiHjq1KlmuX+k31SHH99++2155513RHvhJkyYIO3bt490FuofAQIEQBHQyOFQxesn3CLvbo+zPuquQ22lsjr6osNcN/TfJgeOtZK5BT2s7z9eXisb9pRLv379rOfwQvMI6DCYzi8hR1Nw/XWj0g8//NCs7mrbtq1MnjxZsrN9SysR3CflbggERoAAKDCuXLWBAlOmTPXk9KmWE6cvHAbbuj9V7n92vGw/mCrvb+/8qSsXlrSRDUUdTdnP5w+T5Ts6fer1uj+8vFpk1DUjZfHixTJnzhzZunWrVFVV1b3Mr80swDBYcBtg586dMmvWLDl+/LhJZDhgwACJiWFKaHBbgbs1twCboTZ3C3D/cwJfuus2idv/mnxr7IVB0LmTGvGb8spaGfWzaFm0ZLlJ3Pbxxx+beQ7FxcVmAq5u3qgrXTiaT0A303zuuedkypQpkpCQ0HwP4vidS0pKZNmyZaJJDYcOHSqajJIDgUgVIACK1JYPwXprsrW8flfIU3dUSk5n/3VOPvxqjCR1nyhP/+X5T9Val9/v2LHD9AbpF4IGQvrDxM9PMQXtD9o7p1/IV1zBRHV/o5eXl5s9u/T/sfz8fLn88stNNmd/34frIRBOAgRA4dRaEfCsixYtktsmT5BZ956VzPZND4KmP1ktpxO6y7IVBV4z1x4+fNgEQrt375YOHTqYQEjnpZDmP3gfOt1VXLda0EzDHP4R0J61jRs3yvr166Vnz57Sv39/dmr3Dy1XcUCAAMiBRnStCk8+8Tu5976vyuvfiG1ST9D/LaqVX/+t0iTZS0pK8olJJ4ZqEKRLgY8ePWomheoXR7t27Xx6Pyc1XkDtn3/+eZk6darXYLXxd4isd2pAuXz5cklNTZXBgwezU3tkNT+19UGAAMgHJE4JvsC8eXPlzulT5Cc3V8voPtENegCd8zNzTaK8ub2DzH/r3UbPc9BtADQQ0h8dFtNASFfJxMZ6UkpzBERAl2N36tTJWAfkBhFwUd1a5oMPPjAbzeo8H00zwIEAAhcKEABdaEJJiAisXLlSbr91kmQkH5d7r67w5P7xPiRWVlErr62ulSeXxsjY8RPkiT887ZcJtZodV+dOaCCkE6gzMzPNEFlGRgbzKPz8WdHeN12hd/311/v5yu5frqKiwixrV0Nd1dWrVy+GcN1vdmrYBAECoCbg8dbAC+hS9WeeeUZ+/fMfy6kTpTKsu0j39hWiG5smeDpiTnp2vygsEVlTnCjrCitl7HWfk+989zEz1yEQT6dfMrqEWL+kdb+kuonTycnJgbhdxF1T2/uvf/2r3HrrrRIXZ88LFXEwXiqsE/i3bNkiBQUF0q1bN7nyyiux8+LFSwjUCRAA1Unwa8gLbN68WZYsWSKbN66VA55d3SsqKz3zGlpLdo8cGThosFxzzTVBnedw5MgREwjt2rXLZM7VYEjz2URHN2zILuThg/yACxcuNIkRdaUSh3cB7ZnU4S4NwIcMGSKa0JADAQR8EyAA8s2JsxCwCuhKG81irENkGhTpv8J1vhDbCVjJvL6gPWz6M3bsWK/nRfKLmsBQJzgfO3bMBD46LMuBAAINEyAAapgXZyPgVUD3VaqbOK2TpesmTsfHx3t9Hy+eF9ChRR0Gu+2225hwfp7F/E5tdKhLP2O5ubmSk5PDPJ/PGPFHBHwVIADyVYrzEGiAgE6c3r9/v/mi2rt3r3Tu3NnMF9IVTlFR3jdtbcBtnD11wYIFpiete3fPpC8O0c+TBj2rVq0yw4OazJCM2XwwEGiaAAFQ0/x4NwL1Cui/2nVIR7/AdMfzuonTKSkp9b43Uk/Yvn27aB6bMWPGRCrBuXofOHDAzPNp2bKl2b6CodVzNPwGgSYJEAA1iY83I9AwAc3RooGQbsGhE1Y1GMrKymIjys8wVnomuM+YMcMMg+kXfyQeZWVloqkgDh06ZBIZ6twyDgQQ8J8AAZD/LLkSAj4L6NJlHRrT5fT6BadBkM4XSktL8/karp84f/58EyBG2he/pgJYt26dWdquc3z69u1LgOz6h536NYsAAVCzsHNTBM4L6LCYDvloz5DuPaaBkM59ifQ5Hhoc6jLv0aNHn8dy/HfaM6jzfNLT02XQoEHi6xYujrNQPQQCIkAAFBBWLopA4wR0vocGQrqsXrcw0CEynUAdiZuyatLJF198UaZPn+58D4huxqv5fLRncNiwYWZD3sZ9gngXAgj4KkAA5KsU5yEQRAEdBtEEixoM6Z5kmhRQg6E2bdoE8Sma/1ZvvPGG9O7d2ySYbP6n8f8TlJeXm3k+xcXFMnDgQNPzxypB/ztzRQQuJkAAdDEVyhAIIQFNdqeBkP60bt363LyYSJgcrFs8HDx4UEaNGhVCLdL0R9HkmRs2bDA/umdX//79JRLas+lyXAEB/wkQAPnPkishEFABHR7RzVg1ENIcQ127djXzhTp27BjQ+zbnxU+fPi0zZ86U22+/3ZktRnR4U7M463L2wYMHB3X7luZsS+6NQKgJEACFWovwPAj4IKCBgU6Y1YnCGhjpxGkdJktMTPTh3eF1yty5c6Vfv34S7ts9aAoEneejc5uGDh0qGRkZ4dUQPC0CjgkQADnWoFQn8gR0Gb32Cu3evVu0N0jnCmmw4MrE6U2bNpk91kaOHBmWjasBz+rVq83Edt2pXYNVV9omLBuEh0bgnwIEQHwUEHBEoLq62gRBGgwdPXrUTKjVYKhdu3ZhXUNNE/Dyyy+bYbBwChy0Z27z5s2ydu1ayc7OlgEDBkhcXFxYtwUPj4BLAgRALrUmdUHgnwK6ckwDIf3RXDIaCOmXsG7QGo7H66+/Lnl5eSYlQDg8v87V0uGuVq1ameGuSFu9Fw5txDMiQADEZwABhwV0E01NJqiBkH4p69CYDsFoor1wWm6tK6Z0NdzVV18d0q2lz6gTnI8fP24Cny5duoT08/JwCESyAAFQJLc+dY8oAZ2Lopuy6sRp3aBVe4X0Jzk5OeQdTp48Ka+++qpJihiKw2DquWbNGpPRW5e09+nTh3k+If+p4gEjXYAAKNI/AdQ/IgWOHDliAiFNtnjJJZeYQEiX1UdHR4esx+zZs02yQM2QHSqH9rBpQKmTnNUvPz8/4rcwCZW24TkQqE+AAKg+IV5HwGEBnTi9Z88eM0RWUlJiJk7rEFlqamrI1Vo3CNUd0ocPHx4Sz6a5mHSej86r0u0rQtEsJKB4CARCVIAAKEQbhsdCINgCOsykc4W0RyM+Pt70CummrKGyckknds+ZM8cMgzXn/CUNwlasWCEaMGoiw6ysrGA3FfdDAAE/CBAA+QGRSyDgkoAO62jvhgZDe/fuNSuvdK5Qp06dmn3itM4D0iSCOok72Ifuz6a9ULo9R05OjvTt29f5TVqDbcz9EAimAAFQMLW5FwJhJqCTe+smTmv26bpNWVNSUpqlJgUFBaLPoUNOwTo0INSs26tWrTLZmwcNGmRSCwTr/twHAQQCI0AAFBhXroqAcwK6lYMOj2kwoMkVtVdIh39iYmKCVlddZq47xE+bNi0ovVGHDx+WZcuWmfpp0JWWlha0unIjBBAIrAABUGB9uToCzglohuO6idO6DUe3bt1MMBSM4EB7Yx577DE5UFwkBas/kF2FH8uJUxWi5altWsnl2V1lxLXjZNItUyQ3N7fR9pp9Wnt8iouLzcoznQvVnPOOGl0R3ogAAlYBAiArDS8ggEB9AhoobN++3fQMaU+Q9grpMJlOovbnoavVXnjhBfnRo49IbG2Z3JBTIQOzoqRbWpS08tyqplbkaLnIzoM1smJ3C5m7vqWkpXeR//nZ4zJ69GifH0Xvo0kXN27cKL169RLN6dOyZUuf38+JCCAQPgIEQOHTVjwpAiEtcODAATNxurCwUDRXjwZDnTt3bnJCQF1tddPnx0pl6Q558LpKT+DTwieH+evPyv++FSs3TJgsP//17+oNynQzWV3dpXmRdHWXbmPBgQAC7goQALnbttQMgWYR0InTGkzofCFdMq49QppbqHXr1g1+Hh1qu3rYQJmSd1LuuaamwcNQldW18p8zomXhunLRTNgXW9JfWlpq8vlUVlaaFWYZGRkNfk7egAAC4SdAABR+bcYTIxA2AjppWQMhHSbTAEh7hXTOkC/DSjoBuUOHDvKTyTFyy6CmTbT+wWs1supguny4dtO5rT90NZlmcNYg68orrzRDXszzCZuPFg+KQJMFCICaTMgFEECgPgGdOF1UVGSGyDTH0GWXXWZ6hTp27HjRt2pSxsH5/SQ/7WP5/kT/bM/xyKst5GTKUJkzb4Fs3rxZ1q5da3qnBgwYYLI5X/RBKEQAAWcFCICcbVoqhkBoCmjPi/YIaaJFXb1VN3E6MTHx3APfd8+XJbroJfnm586cK/PHbyY/2VLyRk6VCRMnypAhQ6RNmzb+uCzXQACBMBQgAArDRuOREXBFQJfRayCkc4a0N0iDIe0tGjggRxY/VCMpCVF+req2AzVyx5/jpbDogPxrwOXXm3AxBBAICwECoLBoJh4SAbcFdPl53cTpP/3x95JZtVQeGHthnYtKU2TWij7yxDsDJTmuUm7ov13O1kR5VprVSpvECpk0cItkpR298I3/UnLfC7Fy8z2/kLvvvvtfSvktAghEmgABUKS1OPVFIIQFdEgsPa2tzPr309Il1b7cfeAPvux5/bi88rWXTG0qq6PlS3+aIFuKL5Fl339aEmKrrbV8e/NZmbG1n7z7/mrrObyAAALuC9j/hnG/7tQQAQRCTEBXjCXG1ngNfvSRk+M+PTcoLuasjMnZJcfKE6SwpK3XWg3r3kJWF2wQXa7PgQACkStAABS5bU/NEQg5Ac3A3CO9cX8trS/q4MkMXSq9Mkq81ishNko6tosze5p5PZEXEUDAaYGmJddwmobKIYBAsAU0m3RGim89M5+cTJDXVveS46fjZMXOTnKkLNGTM+htT7LE+p/60nYxovfq3bt3/SdzBgIIOCnQuH9qOUlBpRBAoLkFdG+xhJZnfXoMnfgc6xn6KjmRJCc8QdAnpxLN8JdnGlG9R2KciN6LAwEEIleAHqDIbXtqjkDICSQnJ0t5lW4+Wn8QpKu+dBWY/ujx9NL+8tDMMdKh9UkZ3qPIlNn+c6oy6lxGaNs5lCOAgNsC9AC53b7UDoGwEsjKypKio7GNeuYb+28z73txeU69799z+IzovTgQQCByBQiAIrftqTkCISeQl5cna3aVmwzRDX24yqp/dGjHt7QvgddrlpyolbLyGsnMzGzoLTgfAQQcEiAAcqgxqQoC4S6gO7F37JAm64vsE3l0jk+pZwJ09dnzf31Vn42Sv/y9v7SIqvEMif2jJ8hm8e5HZ+XaUSM9yRPPv992LuUIIOCuAHOA3G1baoZAWApMv/MrMutvP5XczAt7cjQT9HPv5UpZRbxs2pcm9/5lvFRWx8i+T1IkvU2ZPH336zKi116v9X6pIEke/cU9Xs/hRQQQcF+ATNDutzE1RCCsBD755BO5vFumvHLfGcls799emqWe3p+fLsmQzVt30wMUVp8KHhYB/wv4928X/z8fV0QAgQgTaNeunXz74e/Jd2fHN2oukI3rVGWtPDovTn75+BMEPzYkyhGIIAECoAhqbKqKQLgIPPith6R91mB56OVovzxy1dlayXm4Uqbdea+MGzfOL9fkIgggEN4CDIGFd/vx9Ag4K6CJCvtccbn0Ti2Vx6eJp9fGhxTPF9E4faZW7ngqSoaOmSaP/+6pi5xBEQIIRKIAPUCR2OrUGYEwEEhKSpKdu4ukvFWe3D8jVo6dsq8Ms1WnqLRGpvwhVvoNnyS/+s0fbadRjgACEShAABSBjU6VEQgXgejoaFm0+D3pNWy6XPfLaPnju7WebS/qD4QOe3L9/PiNKPm338bIl7/+Q3nqz88x7ydcGp3nRCBIAgyBBQma2yCAQNMEtmzZIj/+7+/JvHnzZXCPeMnvfEqyO0RJSkKUJyeQyLHyWtlxSGTl3iTZ9PEZueP2O+Tbj/yXpKenN+3GvBsBBJwUIAByslmpFALuChw/flwWLlwo7y9dLFs/2iAnPH/WpIapqe2lZ59cGTHyWhk9erTExXl2POVAAAEELAIEQBYYihFAAAEEEEDAXQHmALnbttQMAQQQQAABBCwCBEAWGIoRQAABBBBAwF0BAiB325aaIYAAAggggIBFgADIAkMxAggggAACCLgrQADkbttSMwQQQAABBBCwCBAAWWAoRgABBBBAAAF3BQiA3G1baoYAAggggAACFgECIAsMxQgggAACCCDgrgABkLttS80QQAABBBBAwCJAAGSBoRgBBBBAAAEE3BUgAHK3bakZAggggAACCFgECIAsMBQjgAACCCCAgLsCBEDuti01QwABBBBAAAGLAAGQBYZiBBBAAAEEEHBXgADI3balZggggAACCCBgESAAssBQjAACCCCAAALuChAAudu21AwBBBBAAAEELAIEQBYYihFAAAEEEEDAXQECIHfblpohgAACCCCAgEWAAMgCQzECCCCAAAIIuCtAAORu21IzBBBAAAEEELAIEABZYChGAAEEEEAAAXcFCIDcbVtqhgACCCCAAAIWAQIgCwzFCCCAAAIIIOCuAAGQu21LzRBAAAEEEEDAIkAAZIGhGAEEEEAAAQTcFSAAcrdtqRkCCCCAAAIIWAQIgCwwFCOAAAIIIICAuwIEQO62LTVDAAEEEEAAAYsAAZAFhmIEEEAAAQQQcFeAAMjdtqVmCCCAAAIIIGARIACywFCMAAIIIIAAAu4KEAC527bUDAEEEEAAAQQsAgRAFhiKEUAAAQQQQMBdAQIgd9uWmiGAAAIIIICARYAAyAJDMQIIIIAAAgi4K0AA5G7bUjMEEEAAAQQQsAgQAFlgKEYAAQQQQAABdwUIgNxtW2qGAAIIIIAAAhYBAiALDMUIIIAAAggg4K4AAZC7bUvNEEAAAQQQQMAiQABkgaEYAQQQQAABBNwVIAByt22pGQIIIIAAAghYBAiALDAUI4AAAggggIC7AgRA7rYtNUMAAQQQQAABiwABkAWGYgQQQAABBBBwV4AAyN22pWYIIIAAAgggYBEgALLAUIwAAggggAAC7goQALnbttQMAQQQQAABBCwCBEAWGIoRQAABBBBAwF0BAiB325aaIYAAAggggIBFgADIAkMxAggggAACCLgrQADkbttSMwQQQAABBBCwCBAAWWAoRgABBBBAAAF3BQiA3G1baoYAAggggAACFgECZgEL5wAAA15JREFUIAsMxQgggAACCCDgrgABkLttS80QQAABBBBAwCJAAGSBoRgBBBBAAAEE3BUgAHK3bakZAggggAACCFgECIAsMBQjgAACCCCAgLsCBEDuti01QwABBBBAAAGLAAGQBYZiBBBAAAEEEHBXgADI3balZggggAACCCBgESAAssBQjAACCCCAAALuChAAudu21AwBBBBAAAEELAIEQBYYihFAAAEEEEDAXQECIHfblpohgAACCCCAgEWAAMgCQzECCCCAAAIIuCtAAORu21IzBBBAAAEEELAIEABZYChGAAEEEEAAAXcFCIDcbVtqhgACCCCAAAIWAQIgCwzFCCCAAAIIIOCuAAGQu21LzRBAAAEEEEDAIkAAZIGhGAEEEEAAAQTcFSAAcrdtqRkCCCCAAAIIWAQIgCwwFCOAAAIIIICAuwIEQO62LTVDAAEEEEAAAYsAAZAFhmIEEEAAAQQQcFeAAMjdtqVmCCCAAAIIIGARIACywFCMAAIIIIAAAu4KEAC527bUDAEEEEAAAQQsAgRAFhiKEUAAAQQQQMBdAQIgd9uWmiGAAAIIIICARYAAyAJDMQIIIIAAAgi4K0AA5G7bUjMEEEAAAQQQsAgQAFlgKEYAAQQQQAABdwUIgNxtW2qGAAIIIIAAAhYBAiALDMUIIIAAAggg4K4AAZC7bUvNEEAAAQQQQMAiQABkgaEYAQQQQAABBNwVIAByt22pGQIIIIAAAghYBAiALDAUI4AAAggggIC7AgRA7rYtNUMAAQQQQAABiwABkAWGYgQQQAABBBBwV4AAyN22pWYIIIAAAgggYBEgALLAUIwAAggggAAC7goQALnbttQMAQQQQAABBCwCBEAWGIoRQAABBBBAwF0BAiB325aaIYAAAggggIBFgADIAkMxAggggAACCLgrQADkbttSMwQQQAABBBCwCBAAWWAoRgABBBBAAAF3BQiA3G1baoYAAggggAACFgECIAsMxQgggAACCCDgrgABkLttS80QQAABBBBAwCJAAGSBoRgBBBBAAAEE3BUgAHK3bakZAggggAACCFgECIAsMBQjgAACCCCAgLsCBEDuti01QwABBBBAAAGLAAGQBYZiBBBAAAEEEHBXgADI3balZggggAACCCBgEfh/GxNAY5vlKcQAAAAASUVORK5CYII=" /><!-- --></p> <p>In the example above, the ‘:’ 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="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" 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 ## ## $newgraph ## NULL</code></pre> <p>We can then add the edges required to make the initial graph chordal in a single line:</p> <div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>chordal_graph <span class="ot"><-</span> <span class="fu">add_edges</span>(g1, <span class="fu">is_chordal</span>(g1, <span class="at">fillin=</span><span class="cn">TRUE</span>)<span class="sc">$</span>fillin)</span> <span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(chordal_graph)</span></code></pre></div> <p><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>, i.e. they produce the same graph each single time, e.g. <code>make_tree</code>:</p> <div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>graph1 <span class="ot"><-</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">"undirected"</span>)</span> <span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div> <pre><code>## IGRAPH 46b5a06 U--- 5 3 -- Ring graph ## + attr: name (g/c), mutual (g/l), circular (g/l)</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="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>graph2 <span class="ot"><-</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">"undirected"</span>)</span></code></pre></div> <div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" 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>, i.e. they produce a different graph each time. For instance <code>sample_grg</code>:</p> <div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>graph1 <span class="ot"><-</span> <span class="fu">sample_grg</span>(<span class="dv">100</span>, <span class="fl">0.2</span>)</span> <span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(graph1)</span></code></pre></div> <pre><code>## IGRAPH d62a4d3 U--- 100 526 -- 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="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>graph2 <span class="ot"><-</span> <span class="fu">sample_grg</span>(<span class="dv">100</span>, <span class="fl">0.2</span>)</span> <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" 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, i.e., they are connected in the same way.</p> <div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" 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 (e.g. 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="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</span> <span class="fu">make_graph</span>(<span class="sc">~</span> Alice<span class="sc">-</span>Bob<span class="sc">:</span>Claire<span class="sc">:</span>Frank, Claire<span class="sc">-</span>Alice<span class="sc">:</span>Dennis<span class="sc">:</span>Frank<span class="sc">:</span>Esther,</span> <span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a> George<span class="sc">-</span>Dennis<span class="sc">:</span>Frank, Dennis<span class="sc">-</span>Esther)</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, i.e. 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="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>age <span class="ot"><-</span> <span class="fu">c</span>(<span class="dv">25</span>, <span class="dv">31</span>, <span class="dv">18</span>, <span class="dv">23</span>, <span class="dv">47</span>, <span class="dv">22</span>, <span class="dv">50</span>) </span> <span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"f"</span>, <span class="st">"m"</span>, <span class="st">"f"</span>, <span class="st">"m"</span>, <span class="st">"m"</span>, <span class="st">"f"</span>, <span class="st">"m"</span>)</span> <span id="cb42-3"><a href="#cb42-3" aria-hidden="true" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal <span class="ot"><-</span> <span class="fu">c</span>(<span class="cn">FALSE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">TRUE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">FALSE</span>)</span> <span id="cb42-4"><a href="#cb42-4" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(g)</span></code></pre></div> <pre><code>## IGRAPH a691dbc 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>%\>%</code>:</p> <div class="sourceCode" id="cb44"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</span> <span class="fu">make_graph</span>(<span class="sc">~</span> Alice<span class="sc">-</span>Bob<span class="sc">:</span>Claire<span class="sc">:</span>Frank, Claire<span class="sc">-</span>Alice<span class="sc">:</span>Dennis<span class="sc">:</span>Frank<span class="sc">:</span>Esther,</span> <span id="cb44-2"><a href="#cb44-2" aria-hidden="true" tabindex="-1"></a> George<span class="sc">-</span>Dennis<span class="sc">:</span>Frank, Dennis<span class="sc">-</span>Esther) <span class="sc">%>%</span></span> <span id="cb44-3"><a href="#cb44-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_vertex_attr</span>(<span class="st">"age"</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">%>%</span></span> <span id="cb44-4"><a href="#cb44-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_vertex_attr</span>(<span class="st">"gender"</span>, <span class="at">value =</span> <span class="fu">c</span>(<span class="st">"f"</span>, <span class="st">"m"</span>, <span class="st">"f"</span>, <span class="st">"m"</span>, <span class="st">"m"</span>, <span class="st">"f"</span>, <span class="st">"m"</span>)) <span class="sc">%>%</span></span> <span id="cb44-5"><a href="#cb44-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_edge_attr</span>(<span class="st">"is_formal"</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="cb44-6"><a href="#cb44-6" aria-hidden="true" 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="cb45"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" 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="cb47"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</span>is_formal[<span class="dv">1</span>] <span class="ot"><-</span> <span class="cn">TRUE</span></span> <span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a><span class="fu">E</span>(g)<span class="sc">$</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, e.g. to add a date to the graph:</p> <div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a>g<span class="sc">$</span>date <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"2022-02-11"</span>)</span> <span id="cb49-2"><a href="#cb49-2" aria-hidden="true" tabindex="-1"></a><span class="fu">graph_attr</span>(g, <span class="st">"date"</span>)</span></code></pre></div> <pre><code>## [1] "2022-02-11"</code></pre> <p>To retrieve attributes, you can also use <code>graph_attr</code>, <code>vertex_attr</code>, and <code>edge_attr</code>. To find the ID of a vertex you can use the function <code>match</code>:</p> <div class="sourceCode" id="cb51"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a><span class="fu">match</span>(<span class="fu">c</span>(<span class="st">"George"</span>), <span class="fu">V</span>(g)<span class="sc">$</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="cb53"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>name[<span class="dv">1</span><span class="sc">:</span><span class="dv">3</span>] <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"Alejandra"</span>, <span class="st">"Bruno"</span>, <span class="st">"Carmina"</span>)</span> <span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)</span></code></pre></div> <pre><code>## + 7/7 vertices, named, from a691dbc: ## [1] Alejandra Bruno Carmina Frank Dennis Esther George</code></pre> <p>To delete attributes:</p> <div class="sourceCode" id="cb55"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</span> <span class="fu">delete_vertex_attr</span>(g, <span class="st">"gender"</span>)</span> <span id="cb55-2"><a href="#cb55-2" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</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="cb57"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a><span class="fu">degree</span>(g)</span></code></pre></div> <pre><code>## Alejandra Bruno Carmina Frank Dennis Esther George ## 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="in")</code> and <code>degree(mode="out")</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="cb59"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb59-1"><a href="#cb59-1" aria-hidden="true" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="dv">7</span>)</span></code></pre></div> <pre><code>## George ## 2</code></pre> <div class="sourceCode" id="cb61"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb61-1"><a href="#cb61-1" aria-hidden="true" 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 Frank Dennis ## 4 3 3</code></pre> <p>Most functions that accept vertex IDs also accept vertex <em>names</em> (i.e. the values of the <code>name</code> vertex attribute) as long as the names are unique:</p> <div class="sourceCode" id="cb63"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1" aria-hidden="true" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="at">v=</span><span class="fu">c</span>(<span class="st">"Carmina"</span>, <span class="st">"Frank"</span>, <span class="st">"Dennis"</span>))</span></code></pre></div> <pre><code>## Carmina Frank Dennis ## 4 3 3</code></pre> <p>It also works for single vertices:</p> <div class="sourceCode" id="cb65"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb65-1"><a href="#cb65-1" aria-hidden="true" tabindex="-1"></a><span class="fu">degree</span>(g, <span class="st">"Bruno"</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="cb67"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" 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="cb69"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb69-1"><a href="#cb69-1" aria-hidden="true" tabindex="-1"></a>ebs <span class="ot"><-</span> <span class="fu">edge_betweenness</span>(g)</span> <span id="cb69-2"><a href="#cb69-2" aria-hidden="true" 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,] "Alejandra" "Bruno" ## [2,] "Alejandra" "Carmina"</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="cb71"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb71-1"><a href="#cb71-1" aria-hidden="true" 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="cb73"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb73-1"><a href="#cb73-1" aria-hidden="true" tabindex="-1"></a>graph <span class="ot"><-</span> <span class="fu">graph.full</span>(<span class="at">n=</span><span class="dv">10</span>)</span> <span id="cb73-2"><a href="#cb73-2" aria-hidden="true" tabindex="-1"></a>only_odd_vertices <span class="ot"><-</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="cb73-3"><a href="#cb73-3" aria-hidden="true" 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="cb75"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb75-1"><a href="#cb75-1" aria-hidden="true" tabindex="-1"></a>seq <span class="ot"><-</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="cb75-2"><a href="#cb75-2" aria-hidden="true" tabindex="-1"></a>seq</span></code></pre></div> <pre><code>## + 3/10 vertices, from edec526: ## [1] 2 3 7</code></pre> <div class="sourceCode" id="cb77"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb77-1"><a href="#cb77-1" aria-hidden="true" tabindex="-1"></a>seq <span class="ot"><-</span> seq[<span class="dv">1</span>, <span class="dv">3</span>] <span class="co"># filtering an existing vertex set</span></span> <span id="cb77-2"><a href="#cb77-2" aria-hidden="true" tabindex="-1"></a>seq</span></code></pre></div> <pre><code>## + 2/10 vertices, from edec526: ## [1] 2 7</code></pre> <p>Selecting a vertex that does not exist results in an error:</p> <div class="sourceCode" id="cb79"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb79-1"><a href="#cb79-1" aria-hidden="true" tabindex="-1"></a>seq <span class="ot"><-</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">"foo"</span>, <span class="fl">3.5</span>]</span> <span id="cb79-2"><a href="#cb79-2" aria-hidden="true" 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="cb80"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb80-1"><a href="#cb80-1" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)[age <span class="sc"><</span> <span class="dv">30</span>]<span class="sc">$</span>name</span></code></pre></div> <pre><code>## [1] "Alejandra" "Carmina" "Frank" "Esther"</code></pre> <p>Of course, <code><</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><</code></td> <td>The attribute/property value must be <em>less than</em></td> </tr> <tr class="even"> <td><code><=</code></td> <td>The attribute/property value must be <em>less than or equal to</em></td> </tr> <tr class="odd"> <td><code>></code></td> <td>The attribute/property value must be <em>greater than</em></td> </tr> <tr class="even"> <td><code>>=</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="cb82"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb82-1"><a href="#cb82-1" aria-hidden="true" tabindex="-1"></a><span class="st">`</span><span class="at">%notin%</span><span class="st">`</span> <span class="ot"><-</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="cb83"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb83-1"><a href="#cb83-1" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>degree <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"A"</span>, <span class="st">"B"</span>, <span class="st">"B+"</span>, <span class="st">"A+"</span>, <span class="st">"C"</span>, <span class="st">"A"</span>, <span class="st">"B"</span>)</span> <span id="cb83-2"><a href="#cb83-2" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>degree[<span class="fu">degree</span>(g) <span class="sc">==</span> <span class="dv">3</span>]</span></code></pre></div> <pre><code>## [1] "A" "A+" "C"</code></pre> <div class="sourceCode" id="cb85"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb85-1"><a href="#cb85-1" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>name[<span class="fu">degree</span>(g) <span class="sc">==</span> <span class="dv">3</span>]</span></code></pre></div> <pre><code>## [1] "Alejandra" "Frank" "Dennis"</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. E.g., to select all the edges originating from Carmina (who has vertex index 3):</p> <div class="sourceCode" id="cb87"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb87-1"><a href="#cb87-1" aria-hidden="true" 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 a691dbc (vertex names): ## [1] Alejandra--Carmina Carmina --Frank Carmina --Dennis Carmina --Esther</code></pre> <p>Of course it also works with vertex names:</p> <div class="sourceCode" id="cb89"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb89-1"><a href="#cb89-1" aria-hidden="true" tabindex="-1"></a><span class="fu">E</span>(g)[<span class="fu">.from</span>(<span class="st">"Carmina"</span>)]</span></code></pre></div> <pre><code>## + 4/9 edges from a691dbc (vertex names): ## [1] Alejandra--Carmina Carmina --Frank Carmina --Dennis Carmina --Esther</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, irrespectively 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), Dennis (vertex index 5) and Esther (vertex index 6):</p> <div class="sourceCode" id="cb91"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb91-1"><a href="#cb91-1" aria-hidden="true" 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 a691dbc (vertex names): ## [1] Carmina--Dennis Carmina--Esther Dennis --Esther</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="cb93"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb93-1"><a href="#cb93-1" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"f"</span>, <span class="st">"m"</span>, <span class="st">"f"</span>, <span class="st">"m"</span>, <span class="st">"m"</span>, <span class="st">"f"</span>, <span class="st">"m"</span>)</span></code></pre></div> <div class="sourceCode" id="cb94"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb94-1"><a href="#cb94-1" aria-hidden="true" tabindex="-1"></a>men <span class="ot"><-</span> <span class="fu">V</span>(g)[gender <span class="sc">==</span> <span class="st">"m"</span>]<span class="sc">$</span>name</span> <span id="cb94-2"><a href="#cb94-2" aria-hidden="true" tabindex="-1"></a>men</span></code></pre></div> <pre><code>## [1] "Bruno" "Frank" "Dennis" "George"</code></pre> <div class="sourceCode" id="cb96"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb96-1"><a href="#cb96-1" aria-hidden="true" tabindex="-1"></a>women <span class="ot"><-</span> <span class="fu">V</span>(g)[gender <span class="sc">==</span> <span class="st">"f"</span>]<span class="sc">$</span>name</span> <span id="cb96-2"><a href="#cb96-2" aria-hidden="true" tabindex="-1"></a>women</span></code></pre></div> <pre><code>## [1] "Alejandra" "Carmina" "Esther"</code></pre> <div class="sourceCode" id="cb98"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb98-1"><a href="#cb98-1" aria-hidden="true" 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 a691dbc (vertex names): ## [1] Alejandra--Bruno Alejandra--Frank Carmina --Frank Carmina --Dennis ## [5] Dennis --Esther</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="cb100"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb100-1"><a href="#cb100-1" aria-hidden="true" tabindex="-1"></a><span class="fu">get.adjacency</span>(g)</span></code></pre></div> <pre><code>## 7 x 7 sparse Matrix of class "dgCMatrix" ## Alejandra Bruno Carmina Frank Dennis Esther George ## Alejandra . 1 1 1 . . . ## Bruno 1 . . . . . . ## Carmina 1 . . 1 1 1 . ## Frank 1 . 1 . . . 1 ## Dennis . . 1 . . 1 1 ## Esther . . 1 . 1 . . ## George . . . 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), Frank (index 4), Dennis (index 5) and Esther (index 6), but not to Bruno (index 2) or to George (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 three columns instead of 2.</p> <div class="sourceCode" id="cb102"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb102-1"><a href="#cb102-1" aria-hidden="true" tabindex="-1"></a>layout <span class="ot"><-</span> <span class="fu">layout_with_kk</span>(g)</span></code></pre></div> <p>Some layout algorithms take additional arguments; e.g., 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="cb103"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb103-1"><a href="#cb103-1" aria-hidden="true" tabindex="-1"></a>layout <span class="ot"><-</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="cb104"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb104-1"><a href="#cb104-1" aria-hidden="true" tabindex="-1"></a>layout <span class="ot"><-</span> <span class="fu">layout_with_kk</span>(g)</span></code></pre></div> <div class="sourceCode" id="cb105"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb105-1"><a href="#cb105-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(g, <span class="at">layout =</span> layout, <span class="at">main =</span> <span class="st">"Social network with the Kamada-Kawai layout algorithm"</span>)</span></code></pre></div> <p><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="cb106"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb106-1"><a href="#cb106-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(g, <span class="at">layout =</span> layout_with_fr,</span> <span id="cb106-2"><a href="#cb106-2" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"Social network with the Fruchterman-Reingold layout algorithm"</span>)</span></code></pre></div> <p><img src="data:image/png;base64,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" /><!-- --></p> <p>To improve the visuals, a trivial addition would be to color the vertices according to the gender. We should also try to place the labels slightly outside the vertices to improve readability:</p> <div class="sourceCode" id="cb107"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb107-1"><a href="#cb107-1" aria-hidden="true" tabindex="-1"></a><span class="fu">V</span>(g)<span class="sc">$</span>color <span class="ot"><-</span> <span class="fu">ifelse</span>(<span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="sc">==</span> <span class="st">"m"</span>, <span class="st">"yellow"</span>, <span class="st">"red"</span>)</span> <span id="cb107-2"><a href="#cb107-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(g, <span class="at">layout =</span> layout, <span class="at">vertex.label.dist =</span> <span class="fl">3.5</span>,</span> <span id="cb107-3"><a href="#cb107-3" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"Social network - with genders as colors"</span>)</span></code></pre></div> <p><img src="data:image/png;base64,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" /><!-- --></p> <p>You can also treat the <code>gender</code> attribute as a factor and provide the colors with an argument to <code>plot()</code>, which takes precedence over the <code>color</code> vertex attribute. Colors will be assigned automatically to levels of a factor:</p> <div class="sourceCode" id="cb108"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb108-1"><a href="#cb108-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(g, <span class="at">layout=</span>layout, <span class="at">vertex.label.dist=</span><span class="fl">3.5</span>, <span class="at">vertex.color=</span><span class="fu">as.factor</span>(<span class="fu">V</span>(g)<span class="sc">$</span>gender))</span></code></pre></div> <p><img 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="cb109"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb109-1"><a href="#cb109-1" aria-hidden="true" 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.size=</span><span class="dv">20</span>,</span> <span id="cb109-2"><a href="#cb109-2" aria-hidden="true" tabindex="-1"></a> <span class="at">vertex.color=</span><span class="fu">ifelse</span>(<span class="fu">V</span>(g)<span class="sc">$</span>gender <span class="sc">==</span> <span class="st">"m"</span>, <span class="st">"yellow"</span>, <span class="st">"red"</span>),</span> <span id="cb109-3"><a href="#cb109-3" aria-hidden="true" tabindex="-1"></a> <span class="at">edge.width=</span><span class="fu">ifelse</span>(<span class="fu">E</span>(g)<span class="sc">$</span>is_formal, <span class="dv">5</span>, <span class="dv">1</span>))</span></code></pre></div> <p><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, ie. 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("lgl"))</code></td> <td><code>write_graph(graph, file, format = c("lgl"))</code></td> </tr> <tr class="even"> <td>Adjacency matrix</td> <td><code>adjacency</code></td> <td><code>graph_from_adjacency_matrix(adjmatrix, mode = c("directed", "undirected", "max", "min", "upper","lower", "plus"), weighted = NULL, diag = TRUE, add.colnames = NULL, add.rownames = NA)</code></td> <td><code>as.matrix(graph, "adjacency")</code></td> </tr> <tr class="odd"> <td>DIMACS</td> <td><code>dimacs</code></td> <td><code>read_graph(file, format = c("dimacs"))</code></td> <td><code>write_graph(graph, file, format = c("dimacs"))</code></td> </tr> <tr class="even"> <td>Edge list</td> <td><code>edgelist</code></td> <td><code>read_graph(file, format = c("edgelist"))</code></td> <td><code>write_graph(graph, file, format = c("edgelist"))</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, formati = c("dot"))</code></td> </tr> <tr class="even"> <td>GML</td> <td><code>gml</code></td> <td><code>read_graph(file, format = c("gml"))</code></td> <td><code>write_graph(graph, file, format = c("gml"))</code></td> </tr> <tr class="odd"> <td>GraphML</td> <td><code>graphml</code></td> <td><code>read_graph(file, format = c("graphml"))</code></td> <td><code>write_graph(graph, file, format = c("graphml"))</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("leda"))</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("ncol"))</code></td> <td><code>write_graph(graph, file, format = c("ncol"))</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("pajek"))</code></td> <td><code>write_graph(graph, file, format = c("pajek"))</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://igraph.org/r/html/latest/">https://igraph.org/r/html/latest/</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="cb110"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb110-1"><a href="#cb110-1" aria-hidden="true" tabindex="-1"></a><span class="fu">sessionInfo</span>()</span></code></pre></div> <pre><code>## R version 4.2.1 (2022-06-23) ## Platform: aarch64-apple-darwin20 (64-bit) ## Running under: macOS Ventura 13.1 ## ## Matrix products: default ## BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib ## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib ## ## locale: ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: ## [1] igraph_1.4.1 ## ## loaded via a namespace (and not attached): ## [1] lattice_0.20-45 digest_0.6.29 grid_4.2.1 R6_2.5.1 ## [5] jsonlite_1.8.0 magrittr_2.0.3 evaluate_0.15 highr_0.9 ## [9] stringi_1.7.8 rlang_1.0.6 cachem_1.0.6 cli_3.3.0.9000 ## [13] jquerylib_0.1.4 Matrix_1.4-1 bslib_0.4.0 rmarkdown_2.18 ## [17] tools_4.2.1 stringr_1.4.0 xfun_0.31 yaml_2.3.5 ## [21] fastmap_1.1.0 compiler_4.2.1 pkgconfig_2.0.3 htmltools_0.5.3 ## [25] knitr_1.39 sass_0.4.2</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>