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[igraph] Re: your spinglass.community function in the R igraph library

From: Gábor Csárdi
Subject: [igraph] Re: your spinglass.community function in the R igraph library
Date: Sun, 7 Feb 2010 20:11:34 +0100

Dear Avril,

here are a bunch of examples on how get membership vectors for the
various community finding algorithms. The partitioning with the
maximal modularity score is chosen for the methods that return a full
merge tree.

memberships <- list()


### edge.betweenness.community
ebc <- edge.betweenness.community(G)
mods <- sapply(0:ecount(G), function(i) {
  g2 <- delete.edges(G, ebc$removed.edges[seq(length=i)])
  cl <- clusters(G)$membership
  modularity(G, cl)

g2 <- delete.edges(G, ebc$removed.edges[1:(which.max(mods)-1)])
memberships$`Edge betweenness` <- clusters(g2)$membership


### fastgreedy.community
fc <- fastgreedy.community(G)
memb <- community.to.membership(G, fc$merges,
memberships$`Fast greedy` <- memb$membership


### leading.eigenvector.community
lec <- leading.eigenvector.community(G)
memberships$`Leading eigenvector` <- lec$membership


### spinglass.community
sc <- spinglass.community(G, spins=10)
memberships$`Spinglass` <- sc$membership


### walktrap.community
wt <- walktrap.community(G, modularity=TRUE)
wmemb <- community.to.membership(G, wt$merges,
memberships$`Walktrap` <- wmemb$membership


### label.propagation.community
memberships$`Label propagation` <- label.propagation.community(G)


On Sat, Feb 6, 2010 at 7:39 PM, Coghlan, Avril <address@hidden> wrote:
> Dear Gabor, and igraph developers,
> Thank you Gabor for your helpful reply about spinglass.community(). That 
> makes a lot of sense that it gives different results each time because it is 
> starting from different random start-points each time.
> By the way, I found spinglass.community() very nice because it returns a 
> vector "membership" that tells you which community each vertex belongs to.
> I looked at other igraph functions for finding communities (eg. 
> fastgreedy.community(), walktrap.community()) and found that they don't seem 
> to return any information on which community each vertex is a member of - or 
> at least, I couldn't figure out how to infer that information from their 
> output - is there a way to do that? (sorry if I am being very slow)
> One last thing, I wrote a function that makes a plot of a graph (using 
> Rgraphviz) and plots the communities found by spinglass.community() on the 
> graph, by colouring in the vertices belonging to each particular community in 
> a different colour (eg. one community in red, a second community in green 
> etc.)
> Do you think that this function would be useful enough that it would be worth 
> including in igraph (or in Rgraphviz)? If so, I would be happy to send you 
> the code for my function.
> Kind regards,
> Avril
> Avril Coghlan
> University College Cork, Ireland
> -----Original Message-----
> From: address@hidden [mailto:address@hidden On Behalf Of Gábor Csárdi
> Sent: 19 January 2010 19:41
> To: Coghlan, Avril
> Subject: Re: your spinglass.community function in the R igraph library
> Dear Avril,
> I think you got Jörg's email address wrong, you are supposed to
> replace the 'lastname' part with his last name.
> On Tue, Jan 19, 2010 at 1:11 PM, Coghlan, Avril <address@hidden> wrote:
>> Dear Jorg and Gabor,
>> I have been using your spinglass.community() function in the R "igraph"
>> library for identifying communities in a network, and am finding it very
>> useful.
>> I noticed that sometimes when I run this function repeatedly on the same
>> graph, it gives slightly different answers for the communities.
>> I am wondering if this is expected?
> It is. It is a randomized algorithm, or at least it starts from a
> random configuration of spins.
>> I imagine that you might be using a random number generator in this
>> function, and this is the reason why when you run it again and again on the
>> same input graph, it gives slightly different answers each time - is this
>> right?
> It is perfectly right. The version in the igraph package uses R's
> RNG's so if you set the R RNG seed with 'set.seed()', then you should
> always get the same results. This is useful if you want to reproduce
> things.
> Btw. it is better to write to the igraph-help mailing list, than to me
> directly, see http://igraph.sf.net for details. Thanks.
> Best,
> Gabor
>> Regards,
>> Avril Coghlan
>> University College Cork, Ireland
> --
> Gabor Csardi <address@hidden>     UNIL DGM

Gabor Csardi <address@hidden>     UNIL DGM

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