Re: [igraph] igraph-help Digest, Vol 93, Issue 5

[Bian, Jiang]

It’s not just betweenness. I need a library that is as complete as igraph, but 
can handle huge networks. 
I saw the new xdata-igraph project which seems to be targeting large graphs. 
But there isn’t much information available.

Jiang

On Apr 6, 2014, at 11:00, address@hidden wrote:

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>   1. Large graphs with igraph (Bian, Jiang)
>   2. Re: Large graphs with igraph (=?gb18030?B?y8TV/aOouuzXqaOp?=)
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> ----------------------------------------------------------------------
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> Message: 1
> Date: Sun, 6 Apr 2014 13:43:45 +0000
> From: "Bian, Jiang" <address@hidden>
> To: "address@hidden" <address@hidden>
> Subject: [igraph] Large graphs with igraph
> Message-ID: <address@hidden>
> Content-Type: text/plain; charset="Windows-1252"
> 
> 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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> ------------------------------
> 
> Message: 2
> Date: Sun, 6 Apr 2014 22:37:45 +0800
> From: "=?gb18030?B?y8TV/aOouuzXqaOp?=" <address@hidden>
> To: "=?gb18030?B?SGVscCBmb3IgaWdyYXBoIHVzZXJz?="
>       <address@hidden>
> Subject: Re: [igraph] Large graphs with igraph
> Message-ID: <address@hidden>
> Content-Type: text/plain; charset="gb18030"
> 
> 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
> 
> ----------------------------------------------------------------------
> Confidentiality Notice: This e-mail message, including any attachments, is 
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> and privileged information. Any unauthorized review, use, disclosure or 
> distribution is prohibited. If you are not the intended recipient, please 
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