Game of Thrones Matrix

.block

license: mit

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GoT_interactions_matrix_draft.json

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}

index.html

<!DOCTYPE html>
<html class="ocks-org do-not-copy">
<meta charset="utf-8">
<title>Game of Thrones Co-occurrence</title>
<style>


@import url(https://bost.ocks.org/mike/style.css?aea6f0a);

  
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  fill: #eee;
}

line {
  stroke: #fff;
}

text.active {
  fill: red;
}

</style>
   <script src="//d3js.org/d3.v2.min.js" charset="utf-8"></script>
  
<header>
  <aside>August 18, 2017</aside>
  <a href="../" rel="author">Daniel Licht, d3.js template by Mike Bostock</a>
</header>

<h1><i>Game of Thrones</i> Co-occurrence</h1>

<aside style="margin-top:80px;">
<p>Order: <select id="order">
  <option value="name">by Name</option>
  <option value="count">by Frequency</option>
  <option value="group">by Cluster</option>
</select>

<p>This matrix diagram visualizes character co-occurrences in George R.R. Martin’s Series "A Song of Ice and Fire", books 1-5, commonly called "Game of Thrones"</a></i>.

<p>Each colored cell represents two characters that appeared within n sentences of eachother; darker cells indicate characters that co-occurred more frequently.

<p>Use the drop-down menu to reorder the matrix and explore the data.

<p>Built with <a href="http://d3js.org/">d3.js</a>.
</aside>

<script>

var margin = {top: 80, right: 0, bottom: 10, left: 80},
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var x = d3.scale.ordinal().rangeBands([0, width]),
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var svg = d3.select("body").append("svg")
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d3.json("GoT_interactions_matrix_draft.json", function(miserables) {
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    nodes[link.target].count += link.value;
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    count: d3.range(n).sort(function(a, b) { return nodes[b].count - nodes[a].count; }),
    group: d3.range(n).sort(function(a, b) { return nodes[b].group - nodes[a].group; })
  };

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      .attr("height", height);

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        .on("mouseover", mouseover)
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});

</script>

<p class="attribution">Source: <a href="http://www-cs-staff.stanford.edu/~uno/sgb.html">The Stanford GraphBase</a>.

<p>A network can be represented by an <i><a href="http://en.wikipedia.org/wiki/Adjacency_matrix">adjacency matrix</a></i>, where each cell <i>ij</i> represents an edge from vertex <i>i</i> to vertex <i>j</i>. Here, vertices represent characters in a book, while edges represent co-occurrence in a chapter.

<p>Given this two-dimensional representation of a graph, a natural visualization is to show the matrix! However, the effectiveness of a matrix diagram is heavily dependent on the order of rows and columns: if related nodes are placed closed to each other, it is easier to identify clusters and bridges.

<p>This example lets you try different orderings via the drop-down menu. This type of diagram can be extended with manual reordering of rows and columns, and expanding or collapsing of clusters, to allow deeper exploration. 

<p>While path-following is harder in a matrix view than in a <a href="http://mbostock.github.com/d3/ex/force.html">node-link diagram</a>, matrices have other advantages. As networks get large and highly connected, node-link diagrams often devolve into giant hairballs of line crossings. Line crossings are impossible with matrix views. Matrix cells can also be encoded to show additional data; here color depicts clusters computed by a community-detection algorithm.

<p>Want more? See this analysis of <a href="../shuffle/compare.html">shuffling algorithms</a> using matrix diagrams.

<footer>
  <aside>January 12, 2012</aside>
  <a href="../" rel="author">Daniel Licht, d3.js template by Mike Bostock</a>
</footer>

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style.css

/* Copyright 2013 Michael Bostock. All rights reserved. Do not copy. */

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