Well, betweenness is slow because every paths between every pair of nodes are needed to be recorded. as long as i know, there is no better algorithm than it is used now.
However, some researchers have researched on calculating it on GPGPU, seems interesting, but I have not tried that yet.
------------------ Original ------------------
From: "Bian, Jiang";<address@hidden>;
Date: Sun, Apr 6, 2014 09:43 PM
To: "address@hidden"<address@hidden>;
Subject: [igraph] Large graphs with igraph
Dear all,
I have quite a few big networks (brain connectivity networks, if you care the context) that I need to analysis. On average, each graph has about 50k to 60k nodes, and about 1 billion edges (or more). So, these are not really sparse networks.
Looks like igraph can??t really handle graphs at this scale. e.g., It took over two days to calculate the betweenness centrality (I killed the process, it didn??t finish) on a quad-core machine with 32G ram. I??m running the python binding of igraph, but I doubt it would be too much faster if I change to use the c portion of igraph directly.
I did look into other libraries especially those are built for processing large graphs on a cluster such as graphlab, Spark??s GraphX, Giraph, etc. None of them really has all the algorithms implemented as complete as igraph or NetworkX...
Any suggestions?
Thanks,
Jiang
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