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Marvel Network: A Tricked-Out D3 Implementation

This force-directed graph takes advantage of the new features of D3 version 4 to display and manipulate a network of Marvel Comics characters. Click "open" to use the full suite of tools.

Features

  • Scroll to zoom.
  • Use the slider to change the edge-weight threshold.
  • Click on nodes to see ego networks (click again to see all nodes).
  • Use the dropdown to show three different centrality measures, calculated using NetworkX in Python and imported through the marvel.json file.
<!DOCTYPE html>
<meta charset="utf-8">
<style>
.links line {
stroke: #999;
stroke-opacity: 0.6;
}
.nodes circle {
stroke: #fff;
stroke-width: 1.5px;
}
</style>
<body>
<svg width="960" height="600"></svg>
<script src="https://d3js.org/d3.v4.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.schemeCategory20);
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("body").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("marvel.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.degree; }),d3.max(graph.nodes, function(d) {return d.degree; })])
.range([8,25]);
// Collision detection based on degree centrality.
simulation.force("collide", d3.forceCollide().radius( function (d) { return degreeSize(d.degree); }));
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.degree); })
// Color by group, a result of modularity calculation in NetworkX.
.attr("fill", function(d) { return color(d.group); })
.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.name; });
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 slider (using only d3 and HTML5) that removes nodes below the input threshold.
var slider = d3.select('body').append('p').text('Edge Weight Threshold: ');
slider.append('label')
.attr('for', 'threshold')
.text('1');
slider.append('input')
.attr('type', 'range')
.attr('min', d3.min(graph.links, function(d) {return d.weight; }))
.attr('max', d3.max(graph.links, function(d) {return d.weight; }) / 2)
.attr('value', d3.min(graph.links, function(d) {return d.weight; }))
.attr('id', 'threshold')
.style('width', '50%')
.style('display', 'block')
.on('input', function () {
var threshold = this.value;
d3.select('label').text(threshold);
// Find the links that are at or above the threshold.
var newData = [];
graph.links.forEach( function (d) {
if (d.weight >= threshold) {newData.push(d); };
});
// Data join with only those new links.
link = link.data(newData, function(d) {return d.source + ', ' + d.target;});
link.exit().remove();
var linkEnter = link.enter().append('line').attr('class', 'link');
link = linkEnter.merge(link);
node = node.data(graph.nodes);
// Restart simulation with new link data.
simulation
.nodes(graph.nodes).on('tick', ticked)
.force("link").links(newData);
simulation.alphaTarget(0.1).restart();
});
// A dropdown menu with three different centrality measures, calculated in NetworkX.
// Accounts for node collision.
var dropdown = d3.select('body').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', 'Eigenvector Centrality'])
.enter().append('option')
.attr('value', function(d) { return d.split(' ')[0].toLowerCase(); })
.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.name.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>
{
"directed":false,
"graph":{},
"links":[
{
"source":0,
"target":48,
"weight":74
},
{
"source":0,
"target":71,
"weight":40
},
{
"source":0,
"target":153,
"weight":17
},
{
"source":0,
"target":1,
"weight":52
},
{
"source":0,
"target":154,
"weight":19
},
{
"source":0,
"target":25,
"weight":25
},
{
"source":0,
"target":69,
"weight":24
},
{
"source":0,
"target":158,
"weight":14
},
{
"source":0,
"target":180,
"weight":15
},
{
"source":0,
"target":4,
"weight":26
},
{
"source":0,
"target":161,
"weight":10
},
{
"source":0,
"target":29,
"weight":45
},
{
"source":1,
"target":153,
"weight":16
},
{
"source":1,
"target":48,
"weight":295
},
{
"source":1,
"target":68,
"weight":34
},
{
"source":1,
"target":155,
"weight":11
},
{
"source":1,
"target":12,
"weight":26
},
{
"source":1,
"target":3,
"weight":20
},
{
"source":1,
"target":71,
"weight":123
},
{
"source":1,
"target":188,
"weight":22
},
{
"source":1,
"target":154,
"weight":64
},
{
"source":1,
"target":25,
"weight":87
},
{
"source":1,
"target":89,
"weight":12
},
{
"source":1,
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"weight":67
},
{
"source":1,
"target":67,
"weight":321
},
{
"source":1,
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"weight":58
},
{
"source":1,
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"weight":11
},
{
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"weight":29
},
{
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"weight":16
},
{
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"weight":95
},
{
"source":2,
"target":27,
"weight":10
},
{
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"target":95,
"weight":57
},
{
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},
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{
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"target":97,
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},
{
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"target":98,
"weight":60
},
{
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"target":99,
"weight":61
},
{
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