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    <p>I would strongly advocate you look at <a href="http://networkx.github.com/" rel="noreferrer">NetworkX</a>. It's a battle-tested war horse and the first tool most 'research' types reach for when they need to do analysis of network based data. I have manipulated graphs with 100s of thousands of edges without problem on a notebook. Its feature rich and very easy to use. You will find yourself focusing more on the problem at hand rather than the details in the underlying implementation.</p> <p><strong>Example of <a href="http://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93R%C3%A9nyi_model" rel="noreferrer">Erdős-Rényi</a> random graph generation and analysis</strong></p> <pre><code> """ Create an G{n,m} random graph with n nodes and m edges and report some properties. This graph is sometimes called the Erd##[m~Qs-Rényi graph but is different from G{n,p} or binomial_graph which is also sometimes called the Erd##[m~Qs-Rényi graph. """ __author__ = """Aric Hagberg (hagberg@lanl.gov)""" __credits__ = """""" # Copyright (C) 2004-2006 by # Aric Hagberg # Dan Schult # Pieter Swart # Distributed under the terms of the GNU Lesser General Public License # http://www.gnu.org/copyleft/lesser.html from networkx import * import sys n=10 # 10 nodes m=20 # 20 edges G=gnm_random_graph(n,m) # some properties print "node degree clustering" for v in nodes(G): print v,degree(G,v),clustering(G,v) # print the adjacency list to terminal write_adjlist(G,sys.stdout) </code></pre> <p>Visualizations are also straightforward:</p> <p><img src="https://i.stack.imgur.com/5biM9.jpg" alt="enter image description here"></p> <p>More visualization: <a href="http://jonschull.blogspot.com/2008/08/graph-visualization.html" rel="noreferrer">http://jonschull.blogspot.com/2008/08/graph-visualization.html</a></p>
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    1. POWhat is the most efficient graph data structure in Python?
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    1. CONetworkX is great, but sadly it has issues handling 10^7 nodes. I am routinely going over 16GB RAM with only 2M nodes 15M edges and a few int attributes. Forget about getting anything fancier than that.
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