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Last active Sep 7, 2016
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Miscellany network
Raw
README.md

This bipartite force-directed network graph shows participants in university miscellanies from the 1650s and 1660s. Dark green nodes represent texts published before 1660, while light green nodes represent those published after. The rest of the nodes are for individual contributors (mouseover to see names, scroll or double-click to zoom, click a node to see ego networks). The dark blue nodes show political "shapeshifters" who published in miscellanies both before and after the Restoration. Notice that the graph separates not by political affiliation (Royalist and Republican) but by university affiliation (Oxford and Cambridge).

This visualization is based on a demo from Mike Bostock, on force-directed graphs. Additionally it handles search and a dropdown menu, which allows you to switch between different measures of centrality (degree, betweenness, and closeness) without reloading the graph. (All centrality calculations were made using the bipartite algorithms in Python's NetworkX.)

Raw
index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
.links line {
stroke: #999;
stroke-opacity: 0.6;
}
.nodes circle {
stroke: #fff;
stroke-width: 1.5px;
}
#tools div {
float: right;
display: inline;
}
form { display: inline; }
</style>
<body>
<div id='tools'>
</div>
<svg width="960" height="600"></svg>
<script src="https://d3js.org/d3.v4.min.js"></script>
<script src="https://d3js.org/d3-scale-chromatic.v1.min.js"></script>
<script>
var svg = d3.select("svg"),
width = +svg.attr("width"),
height = +svg.attr("height");
// Call zoom for svg container.
svg.call(d3.zoom().on('zoom', zoomed));
var color = d3.scaleOrdinal(d3.schemePaired);
var simulation = d3.forceSimulation()
.force("link", d3.forceLink())//Or to use names rather than indices: .id(function(d) { return d.id; }))
.force("charge", d3.forceManyBody().strength([-120]).distanceMax([500]))
.force("center", d3.forceCenter(width / 2, height / 2));
var container = svg.append('g');
// Create form for search (see function below).
var search = d3.select("div#tools").append('form').attr('onsubmit', 'return false;');
var box = search.append('input')
.attr('type', 'text')
.attr('id', 'searchTerm')
.attr('placeholder', 'Type to search...');
var button = search.append('input')
.attr('type', 'button')
.attr('value', 'Search')
.on('click', function () { searchNodes(); });
// Toggle for ego networks on click (below).
var toggle = 0;
d3.json("miscellany_network.json", function(error, graph) {
if (error) throw error;
// Make object of all neighboring nodes.
var linkedByIndex = {};
graph.links.forEach(function(d) {
linkedByIndex[d.source + ',' + d.target] = 1;
linkedByIndex[d.target + ',' + d.source] = 1;
});
// A function to test if two nodes are neighboring.
function neighboring(a, b) {
return linkedByIndex[a.index + ',' + b.index];
}
// Linear scale for degree centrality.
var degreeSize = d3.scaleLinear()
.domain([d3.min(graph.nodes, function(d) {return d.degreecentrality; }),d3.max(graph.nodes, function(d) {return d.degreecentrality; })])
.range([8,25]);
// Collision detection based on degree centrality.
simulation.force("collide", d3.forceCollide().radius( function (d) { return degreeSize(d.degreecentrality); }));
var link = container.append("g")
.attr("class", "links")
.selectAll("line")
.data(graph.links, function(d) { return d.source + ", " + d.target;})
.enter().append("line")
.attr('class', 'link');
var node = container.append("g")
.attr("class", "nodes")
.selectAll("circle")
.data(graph.nodes)
.enter().append("circle")
// Calculate degree centrality within JavaScript.
//.attr("r", function(d, i) { count = 0; graph.links.forEach(function(l) { if (l.source == i || l.target == i) { count += 1;}; }); return size(count);})
// Use degree centrality from NetworkX in json.
.attr('r', function(d, i) { return degreeSize(d.degreecentrality); })
// Color by bipartite indicator.
//.attr("fill", function(d) { return color(d.bipartite); })
.style("fill", function(d) { if (d.date >= 1660) {return color(0);} else if (d.date < 1660) {return color(1);} else if (d.shapeshifter == 1) {return color(2);} else {return color(3);} })
.attr('class', 'node')
// On click, toggle ego networks for the selected node.
.on('click', function(d, i) {
if (toggle == 0) {
// Ternary operator restyles links and nodes if they are adjacent.
d3.selectAll('.link').style('stroke-opacity', function (l) {
return l.target == d || l.source == d ? 1 : 0.1;
});
d3.selectAll('.node').style('opacity', function (n) {
return neighboring(d, n) ? 1 : 0.1;
});
d3.select(this).style('opacity', 1);
toggle = 1;
}
else {
// Restore nodes and links to normal opacity.
d3.selectAll('.link').style('stroke-opacity', '0.6');
d3.selectAll('.node').style('opacity', '1');
toggle = 0;
}
})
.call(d3.drag()
.on("start", dragstarted)
.on("drag", dragged)
.on("end", dragended));
node.append("title")
.text(function(d) { return d.id; });
simulation
.nodes(graph.nodes)
.on("tick", ticked);
simulation.force("link")
.links(graph.links);
function ticked() {
link
.attr("x1", function(d) { return d.source.x; })
.attr("y1", function(d) { return d.source.y; })
.attr("x2", function(d) { return d.target.x; })
.attr("y2", function(d) { return d.target.y; });
node
.attr("cx", function(d) { return d.x; })
.attr("cy", function(d) { return d.y; });
}
// A dropdown menu with three different centrality measures, calculated in NetworkX.
// Accounts for node collision.
var dropdown = d3.select('div#tools').append('div')
.append('select')
.on('change', function() {
var centrality = this.value;
var centralitySize = d3.scaleLinear()
.domain([d3.min(graph.nodes, function(d) { return d[centrality]; }), d3.max(graph.nodes, function(d) { return d[centrality]; })])
.range([8,25]);
node.attr('r', function(d) { return centralitySize(d[centrality]); } );
// Recalculate collision detection based on selected centrality.
simulation.force("collide", d3.forceCollide().radius( function (d) { return centralitySize(d[centrality]); }));
simulation.alphaTarget(0.1).restart();
});
dropdown.selectAll('option')
.data(['Degree Centrality', 'Betweenness Centrality', 'Closeness Centrality'])
.enter().append('option')
.attr('value', function(d) { return d.split(' ')[0].toLowerCase()+'centrality'; })
.text(function(d) { return d; });
});
function dragstarted(d) {
if (!d3.event.active) simulation.alphaTarget(0.3).restart();
d.fx = d.x;
d.fy = d.y;
}
function dragged(d) {
d.fx = d3.event.x;
d.fy = d3.event.y;
}
function dragended(d) {
if (!d3.event.active) simulation.alphaTarget(0);
d.fx = null;
d.fy = null;
}
// Zooming function translates the size of the svg container.
function zoomed() {
container.attr("transform", "translate(" + d3.event.transform.x + ", " + d3.event.transform.y + ") scale(" + d3.event.transform.k + ")");
}
// Search for nodes by making all unmatched nodes temporarily transparent.
function searchNodes() {
var term = document.getElementById('searchTerm').value;
var selected = container.selectAll('.node').filter(function (d, i) {
return d.id.toLowerCase().search(term.toLowerCase()) == -1;
});
selected.style('opacity', '0');
var link = container.selectAll('.link');
link.style('stroke-opacity', '0');
d3.selectAll('.node').transition()
.duration(5000)
.style('opacity', '1');
d3.selectAll('.link').transition().duration(5000).style('stroke-opacity', '0.6');
}
</script>
</body>
Raw
miscellany_network.json
{
"directed": false,
"graph": {},
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},
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