[igraph] Large graphs with igraph

[Bian, Jiang]

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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