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";<[email protected]>;
Date: Sun, Apr 6, 2014 09:43 PM
To: "[email protected]"<[email protected]>;
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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