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Revision 8f729641930a552a9d05433097bf55cdfd1ce980 authored by Marko A. Rodriguez on 29 March 2013, 16:05:02 UTC, committed by Marko A. Rodriguez on 29 March 2013, 16:05:02 UTC 
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Generating-Artificial-Natural-Graphs.html 
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<div id="head"><h3><a href="Home.html">Aurelius Titan 0.3.0</a></h3>
  <h1>Generating Artificial Natural Graphs</h1>
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<p>Real-world graphs are not <a href="http://en.wikipedia.org/wiki/Random_graph">random</a>. Example real-world graphs include social graphs, word graphs, neural graphs, airline graphs, water flow graphs, etc. Interestingly enough, there is a simple statistical understanding of most natural graphs. In short, many vertices have few connections and very few vertices have many connections. A popular algorithm to generate a graph that has this connectivity pattern is known as the <a href="http://en.wikipedia.org/wiki/Preferential_attachment">preferential attachment</a> algorithm which can be simply described with the colloquial phrase: “the rich get richer.” This section provides some simple code to artificially generate a natural looking graph in Titan using <a href="http://gremlin.tinkerpop.com">Gremlin</a>.</p>
<p><br /><br /></p>
<h2>Generating a Graph with Natural Statistics<a class="anchor" id="Generating-a-Graph-with-Natural-Statistics" href="#Generating-a-Graph-with-Natural-Statistics"></a></h2>
<p>The first thing to do is to connect to a Cassandra cluster with Titan. In the example below, a local connection is used where <code>storage.batch-loading</code> ensures more speedy performance.</p>
<div class="highlight"><pre>conf = new BaseConfiguration()
conf.setProperty('storage.backend','cassandra')
conf.setProperty('storage.hostname','127.0.0.1')
conf.setProperty('storage.batch-loading','true');
g = TitanFactory.open(conf)
</pre></div>
<p>Next, the following script generates a graph with <code>size</code> number of edges.</p>
<div class="highlight"><pre>size = 100000; ids = [g.addVertex().id]; rand = new Random();
(1..size).each{
  v = g.addVertex();
  u = g.v(ids.get(rand.nextInt(ids.size())))
  g.addEdge(v,u,'linked');
  ids.add(u.id);
  ids.add(v.id);
  if(it % 1000 == 0) 
    g.commit()
}
</pre></div>
<h2>Computing the In-Degree Distribution of the Graph<a class="anchor" id="Computing-the-In-Degree-Distribution-of-the-Graph" href="#Computing-the-In-Degree-Distribution-of-the-Graph"></a></h2>
<div class="highlight"><pre>indegree = [:].withDefault{0}
ids.unique().each{ 
  count = g.v(it).inE.count();
  indegree[count] = indegree[count] + 1;
}
indegree.sort{a,b -&gt; b.value &lt;=&gt; a.value}
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  <p id="last-edit">Last edited by <b>mbroecheler</b>, 2013-03-22 02:17:25</p>
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