http://www.sciencedirect.com/science/article/pii/S0370157309002841
Figure 38-40 show some example of visualization of big network using community detection in section XVII. You can follow the reference for more detail. 在 2012年4月5日 下午7:33，Peter Flom <[email protected]>写道： > Thanks. Have you got a reference or link or something where I can read > more about this?**** > > ** ** > > Peter Flom**** > > Peter Flom Consulting**** > > http://www.statisticalanalysisconsulting.com/**** > > http://www.IAmLearningDisabled.com**** > > ** ** > > *From:* > igraph-help-bounces+peterflomconsulting=mindspring....@nongnu.org[mailto: > igraph-help-bounces+peterflomconsulting=mindspring....@nongnu.org] *On > Behalf Of *?? > *Sent:* Wednesday, April 04, 2012 10:33 PM > > *To:* Help for igraph users > *Subject:* Re: [igraph] Working with large networks and how to sample > from a graph?**** > > ** ** > > Perhaps you can try community network for visualization of those big > network, in which a vertex represent a community. > > evan**** > > 在 2012年4月5日 上午1:46，Peter Flom <[email protected]>写道：**** > > Thanks > > These big networks are hard! My past experience is with networks of a > couple > hundred nodes, at most**** > > > Peter > > Peter Flom > Peter Flom Consulting > http://www.statisticalanalysisconsulting.com/ > http://www.IAmLearningDisabled.com**** > > -----Original Message----- > From: igraph-help-bounces+peterflomconsulting=mindspring....@nongnu.org > [mailto:igraph-help-bounces+peterflomconsulting=mindspring....@nongnu.org] > **** > > On Behalf Of Gábor Csárdi > Sent: Wednesday, April 04, 2012 1:34 PM > To: Help for igraph users > Subject: Re: [igraph] Working with large networks and how to sample from a > graph?**** > > On Wed, Apr 4, 2012 at 7:26 AM, Tamás Nepusz <[email protected]> wrote: > >> One idea I had was to take a small random sample from the network (say > 5,000 nodes) but I am not sure exactly how to do this in igraph. > > > > Well, it depends on how you want to do it. You can try selecting 5000 > nodes randomly from the entire network and then take the subgraph; this is > relatively simple: > > > > library(igraph) > > vs <- sample.int(vcount(g), 5000)-1 > > g2 <- subgraph(g, vs) > > > > However, if your graph is large and sparse enough, there is a chance that > the resulting graph will not be connected at all, and then your estimates > will bear no resemblance at all to the "real" betweenness values. > > Well, I'm not convinced that there is any kind of sampling that will tell > you much about betweenness values in the original network. > (Unless you network structure is special and you can use this fact in the > sampling.) I would recommend doing some simulations first, with > (say) snowball sampling. > > Gabor > > [...] > -- > Gabor Csardi <[email protected]> MTA KFKI RMKI > > _______________________________________________ > 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**** > > ** ** > > _______________________________________________ > igraph-help mailing list > [email protected] > https://lists.nongnu.org/mailman/listinfo/igraph-help > >
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