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, [email protected] wrote: > Send igraph-help mailing list submissions to > [email protected] > > 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 > [email protected] > > You can reach the person managing the list at > [email protected] > > 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" <[email protected]> > To: "[email protected]" <[email protected]> > Subject: [igraph] Large graphs with igraph > Message-ID: <[email protected]> > 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?=" <[email protected]> > To: "=?gb18030?B?SGVscCBmb3IgaWdyYXBoIHVzZXJz?=" > <[email protected]> > Subject: Re: [igraph] Large graphs with igraph > Message-ID: <[email protected]> > 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";<[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 > > ---------------------------------------------------------------------- > 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 > [email protected] > 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 > [email protected] > https://lists.nongnu.org/mailman/listinfo/igraph-help > > > End of igraph-help Digest, Vol 93, Issue 5 > ****************************************** _______________________________________________ igraph-help mailing list [email protected] https://lists.nongnu.org/mailman/listinfo/igraph-help
