Hi, I'm sorry, there was obviously a mistake in my codes. These codes would be the ones I used:
# Test: small network - undirceted_unwweighted_connected - FASTGREEDY
adjacency_undirected_unweighted_connected<-read.table("test_smaller_nw_adjacency_undirected_unweighted_connected.txt")
adjacency_matrix_undirected_unweighted_connected<-as.matrix(read.table("test_smaller_nw_adjacency_undirected_unweighted_connected.txt"))
g_adjacency_undirected_weighted_connected <-
graph.adjacency(adjacency_matrix_undirected_unweighted_connected)
fg_undirected_unweighted_connected<-fastgreedy.community(g_adjacency_undirected_weighted_connected)
fg_undirected_unweighted_connected
membership(fg_undirected_unweighted_connected)
sizes(fg_undirected_unweighted_connected)
Regards,
Stefanie
From: stefanie_...@hotmail.com
To: igraph-help@nongnu.org
Subject: RE: [igraph] Fastgreedy Algorithm for Community Detection in igraph
for RStudio - fatal error.
Date: Wed, 28 Nov 2012 14:09:43 +0100
Hi,
thanks a lot! I send you the small network as well as the codes I used in the
attachement. Remark: I changed the fact that it's an undirected graph, manually
in R: Direction - from TRUE to FALSE.
Regards,
Stefanie
> From: nta...@gmail.com
> Date: Wed, 28 Nov 2012 11:44:39 +0100
> To: igraph-help@nongnu.org
> Subject: Re: [igraph] Fastgreedy Algorithm for Community Detection in igraph
> for RStudio - fatal error.
>
> Hi,
>
> > Is it possible, that I have a newer version of igraph or RStudio, which is
> > not compatible with the fastgreedy alg.? What can I do?
> I strongly suspect that the problem lies in RStudio itself because all the
> community detection algorithms work perfectly fine for me if I use them from
> the command line version of R. But just to make sure, please send me the
> small network that you managed to crash RStudio with and I'll check on my
> machine.
>
> > Then I have a second question: I’m also using the Optimal Modularity
> > Algorithm. But it needs a lot of time to compute (I started it 24 hours ago
> > and it’s still computing). Is there a possibility to make it faster?
> No, the optimal modularity algorithm is pretty much unusable for larger
> graphs (more than 40-50 nodes I guess). This is because it uses integer
> programming in the background and solving a general integer programming
> problem has exponential time complexity. On the other hand, if the algorithm
> finishes, it will give you the global optimum (unlike other community
> detection algorithms which usually do not have any performance guarantees).
>
> --
> T.
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> igraph-help@nongnu.org
> https://lists.nongnu.org/mailman/listinfo/igraph-help
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test_smaller_nw_adjacency_undirected_unweighted_connected.xlsx
Description: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
# Test: small network - undirceted_unwweighted_connected
adjacency_undirected_unweighted_connected<-read.table("test_smaller_nw_adjacency_undirected_unweighted_connected.txt")
adjacency_matrix_undirected_unweighted_connected<-as.matrix(read.table("test_smaller_nw_adjacency_undirected_unweighted_connected.txt"))
g_adjacency_undirected_weighted_connected <-
graph.adjacency(adjacency_matrix_undirected_unweighted_connected)
fg_undirected_unweighted_connected<-fastgreedy.community(g_adjacency_undirected_weighted_connected)
g_adjacency_undirected_weighted_connected
membership(g_adjacency_undirected_weighted_connected)
sizes(g_adjacency_undirected_weighted_connected)
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