[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: [igraph] igraph-help Digest, Vol 93, Issue 5
From: |
Bian, Jiang |
Subject: |
Re: [igraph] igraph-help Digest, Vol 93, Issue 5 |
Date: |
Sun, 6 Apr 2014 16:43:22 +0000 |
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:
> Send igraph-help mailing list submissions to
> address@hidden
>
> To subscribe or unsubscribe via the World Wide Web, visit
> https://lists.nongnu.org/mailman/listinfo/igraph-help
> or, via email, send a message with subject or body 'help' to
> address@hidden
>
> You can reach the person managing the list at
> address@hidden
>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of igraph-help digest..."
>
>
> Today's Topics:
>
> 1. Large graphs with igraph (Bian, Jiang)
> 2. Re: Large graphs with igraph (=?gb18030?B?y8TV/aOouuzXqaOp?=)
>
>
> ----------------------------------------------------------------------
>
> 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
>
> ----------------------------------------------------------------------
> Confidentiality Notice: This e-mail message, including any attachments, is
> for the sole use of the intended recipient(s) and may contain confidential
> and privileged information. Any unauthorized review, use, disclosure or
> distribution is prohibited. If you are not the intended recipient, please
> contact the sender by reply e-mail and destroy all copies of the original
> message.
>
>
>
> ------------------------------
>
> 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
> for the sole use of the intended recipient(s) and may contain confidential
> and privileged information. Any unauthorized review, use, disclosure or
> distribution is prohibited. If you are not the intended recipient, please
> contact the sender by reply e-mail and destroy all copies of the original
> message.
>
> _______________________________________________
> igraph-help mailing list
> address@hidden
> https://lists.nongnu.org/mailman/listinfo/igraph-help
> .
> -------------- next part --------------
> An HTML attachment was scrubbed...
> URL:
> <http://lists.nongnu.org/archive/html/igraph-help/attachments/20140406/5d616417/attachment.html>
>
> ------------------------------
>
> _______________________________________________
> igraph-help mailing list
> address@hidden
> https://lists.nongnu.org/mailman/listinfo/igraph-help
>
>
> End of igraph-help Digest, Vol 93, Issue 5
> ******************************************
- Re: [igraph] igraph-help Digest, Vol 93, Issue 5,
Bian, Jiang <=