Hi Tamas, everyone, I just want to report, that igraph-R Infomap has still not finished computing after 165,657 minutes (16+ weeks). Compared to the same community detection done in C++ implementation (4 min) this is a factor of 41414. That makes me wonder whether or not the Infomap implementation in igraph may have a problem. It does seem to work fine for small graphs but does not stop computing in a reasonable time for large graphs. Is there a way for me to provide you with more detailed information? Should I open an issue somewhere and upload files?
Many thanks for more information and best regards, Alexander > On 23 Mar 2016, at 17:33, Alexander Struck > <[email protected]> wrote: > > Hi Tamas, > > thank you for your response. I remember the conversation where Gabor brought > up the GPL vs AGPL issue. I did not expect changes in this respect. I was > rather wondering: Is the older Infomap version implemented in igraph-R using > a fast language like C? The run time difference between the old igraph-R > implementation (113+ hours) and the latest C++ (4 min) is currently at a > factor of about 1700. I wasn’t expecting this performance jump between an > older and newer C++ Implementation. > > For the time being I will assume that the older version is implemented in > igraph-R using C++. And I will report back if this process ever stops by > itself. > > Thanks again, > > Alexander > > > > >> On 23 Mar 2016, at 11:06, Tamas Nepusz <[email protected]> wrote: >> >> Hi Alexander, >> >> The problem here is that igraph contains an _older_ version of the >> InfoMap code with a slower implementation. The old implementation was >> released under the GNU GPL (if I remember correctly), so we could >> simply include it in igraph. The new implementation is licensed under >> GNU AGPL, which is incompatible with GNU GPL, meaning that we cannot >> include it in igraph unless we re-license igraph under GNU AGPL. (Or, >> at least, that's what I was told, but I'm no lawyer). >> >> T. >> >> >> On Wed, Mar 23, 2016 at 9:22 AM, Alexander Struck >> <[email protected]> wrote: >>> Dear all, >>> >>> I would appreciate some expectation setting regarding the igraph port of >>> Infomap. I have an Infomap process running that works on a directed network >>> of 1,282,336 nodes and 2,507,034 links. Running time exceeds 100 hours >>> using igraph. The C++ implementation from http://mapequation.org/code.html >>> finished community detection in 4 min 42 sec on the same machine. My naive >>> expectation would have been that any partitioning algorithm that is >>> supposed to run on large complex networks is implemented in a fast language >>> and made available to igraph using interfaces to these languages. I’m no >>> expert on this and have to rely on others to do the actual interfacing work >>> but where went my expectation wrong? >>> >>> Many thanks and best regards, >>> >>> Alexander >>> >>>> sessionInfo() >>> R version 3.2.2 (2015-08-14) >>> Platform: x86_64-pc-linux-gnu (64-bit) >>> Running under: Ubuntu precise (12.04.5 LTS) >>> other attached packages: >>> [1] igraph_1.0.1 >>> >>> >>>> On 22 Mar 2016, at 22:33, Tamas Nepusz <[email protected]> wrote: >>>> >>>> Hi, >>>> >>>> Analysing a graph of a few million vertices and edges should not be a >>>> problem for igraph, although not all methods are suited for this. The >>>> "fast greedy" method and the Louvain method (also known as >>>> "multilevel" in igraph) probably works fine. InfoMap and walktrap >>>> might probably take a bit more time. However, note that none of these >>>> methods (except InfoMap) were explicitly designed for directed graphs, >>>> so the result might or might not make sense in the end. >>>> >>>> For reference, the "fast greedy" method ran to completion using >>>> igraph's Python interface in less than two minutes for an Erdos-Renyi >>>> random network with 1.5 million vertices and 5 million edges, although >>>> the graph was undirected in this case (because the "fast greedy" >>>> method does not handle directed graphs anyway). >>>> >>>> So, all in all, I don't think you should be having problems with a >>>> graph of this size, unless there is something wrong with the R >>>> interface of igraph (I was trying the Python interface because I'm >>>> more familiar with that one) or unless Rgui is doing something that it >>>> shouldn't be doing. If you can upload your graph somewhere, I can try >>>> and give it a go with R (without the GUI) on a Linux machine. >>>> >>>> T. >>>> >>>> >>>> On Tue, Mar 22, 2016 at 1:34 PM, AaaSDFfff <[email protected]> wrote: >>>>> Hi everyone! >>>>> >>>>> I recently started using the R language and the igraph package. I use >>>>> these >>>>> tools to create a directed graph with edge weight attribute containing >>>>> about >>>>> 1.2 million vertices and 5 million edges. Creating this kind of graph is >>>>> easy and really fast. But after I start the community detection on this >>>>> graph the Rgui always freezes out after about 2 or 3 hours and never >>>>> returns >>>>> with the results. The command what I use is this: >>>>> >>>>> clust = groups(cluster_label_prop(g, weights=E(g)$weight)) or clust = >>>>> cluster_label_prop(g, weights=E(g)$weight) >>>>> >>>>> I tried other comm. det. methods such as walktrap, spinglass or mapinfo >>>>> but >>>>> there were the same results. The computer I'm using has: >>>>> >>>>> - win7 64bit >>>>> - 12 Gbyte RAM >>>>> - 3.2.3 R 64bit >>>>> - 1.0.1 igraph >>>>> >>>>> When I use the the mentioned command on a directed graph with edge weight >>>>> attribute containing about 50.000 vertices and 2 million edges the comm. >>>>> det. returns with the results after few minutes. >>>>> >>>>> My question is: can somebody gime me an advice about what i should do to >>>>> make the comm. det. runable and faster? >>>>> Thx for your answers! >>>>> >>>>> Best regards, >>>>> Adam >>>>> >>>>> Ps.: Sorry for my english, unfortunatelly I don't have to use it often and >>>>> I'm not a native speaker >>>>> >>>>> _______________________________________________ >>>>> igraph-help mailing list >>>>> [email protected] >>>>> https://lists.nongnu.org/mailman/listinfo/igraph-help >>>>> >>>> >>>> _______________________________________________ >>>> igraph-help mailing list >>>> [email protected] >>>> https://lists.nongnu.org/mailman/listinfo/igraph-help >>> >>> > Image Knowledge Gestaltung. An Interdisciplinary Laboratory Cluster of Excellence Humboldt-Universität zu Berlin Alexander Struck Data Scientist Head of IT Phone: +49 30 2093 66177 E-Mail: [email protected] URL: www.interdisciplinary-laboratory.hu-berlin.de Street Address: Sophienstrasse 22a, D-10178 Berlin Postal Address: Unter den Linden 6, D-10099 Berlin
signature.asc
Description: Message signed with OpenPGP using GPGMail
_______________________________________________ igraph-help mailing list [email protected] https://lists.nongnu.org/mailman/listinfo/igraph-help
