Hi Tamás, First of all thank you for your reply and again I also would like to
thank you for the personal consult. We tried your solution and after we removed
the name attribute from our graph it seems like the calculations will be done
within a reasonable time. However, when we run the exact codes of yours we got
quite different results: > n = 15000> radius = 0.2 / ((n/100) ** 0.5)> g =
grg.game(n, radius)> cl = label.propagation.community(g)>
system.time(lapply(groups(cl), function(x){induced.subgraph(g, x)})) user
system elapsed 1.14 0.00 1.14> V(g)$name = 10000000:(10000000+n-1)> cl
= label.propagation.community(g)> system.time(lapply(groups(cl),
function(x){induced.subgraph(g, x)})) user system elapsed 8.86 0.08
8.93> V(g)$name = sapply(10000000:(10000000+n-1), toString)> cl =
label.propagation.community(g)> system.time(lapply(groups(cl),
function(x){induced.subgraph(g, x)})) user system elapsed 1.46 0.04
1.51> We got the biggest slowdown when the type of the name attribute was
numeric instead of using string attribute as you did at the third time. And
there is another odd thing using the authority.score function. First, below you
can see our script : # deleting variablesrm(list=ls()) # if not installed then
install.packages("igraph")# if not installed then
install.packages("plyr")library("igraph")library("plyr") # set working
diractorysetwd("********************") # reading and creating graphg_in =
read.csv("SNA_05_Net.csv", sep=" ")g = graph.data.frame(g_in, directed=FALSE)
node_list = as.matrix(as.numeric(V(g)$name))write.table(node_list,
file="SNA_Node_List.csv", quote=FALSE, sep="§", col.names=FALSE) g =
remove.vertex.attribute(g, "name") # creating clusters -- cluster_optimal?clust
= groups(cluster_label_prop(g, weights=E(g)$weight)) # exporting clusterscl =
ldply(clust, data.matrix)write.table(as.matrix(cl), file="sna_R.csv",
quote=FALSE, sep="§", col.names=FALSE) # creating sub-graphsg_sub =
lapply(clust, function(x){induced.subgraph(g, x)}) # creating cluster/subgraph
KPIsauth = lapply(g_sub, function(x){authority_score(x)$vector})auth_scr =
ldply(auth, data.matrix) write.table(as.matrix(auth_scr),
file="sna_R_ath_scr.csv", quote=FALSE, sep="§", col.names=FALSE) edg =
lapply(g_sub, function(x){edge_density(x, loops=FALSE)})edg_dens = ldply(edg,
data.matrix) write.table(edg_dens, file="sna_R_edg_dens.csv", quote=FALSE,
sep="§", col.names=FALSE) dgr_o = lapply(g_sub, function(x){degree(x,
mode=c("out"), loops=FALSE)})dgr_out = ldply(dgr_o, data.matrix)
write.table(as.matrix(dgr_out), file="sna_R_dgr_out.csv", quote=FALSE,
sep="§", col.names=FALSE) dgr_i = lapply(g_sub, function(x){degree(x,
mode=c("in"), loops=FALSE)})dgr_in = ldply(dgr_i, data.matrix)
write.table(as.matrix(dgr_in), file="sna_R_dgr_in.csv", quote=FALSE,
sep="§", col.names=FALSE) eigv = lapply(g_sub,
function(x){eigen_centrality(x)$vector})eigv_cent = ldply(eigv, data.matrix)
write.table(as.matrix(eigv_cent), file="sna_R_eigv_cent.csv", quote=FALSE,
sep="§", col.names=FALSE) el = lapply(g_sub,
function(x){as_edgelist(x)})edg_list = ldply(el, data.matrix)
write.table(as.matrix(edg_list), file="sna_R_subgraps.csv", quote=FALSE,
sep="§", col.names=FALSE) Everything is working fine except the
„lapply(g_sub, function(x){authority_score(x)$vector})” statement,
because there is one group where the authority_score function fails. This
cheeky bastard is the number 293863 cluster. If I run the
„lapply(g_sub[1:293862], function(x){authority_score(x)$vector})”
or the „lapply(g_sub[293864:length(g_sub)],
function(x){authority_score(x)$vector})” statements they are working fine
but when I run the „lapply(g_sub,
function(x){authority_score(x)$vector})” statement I got the same error
message when I run „lapply(g_sub[293863],
function(x){authority_score(x)$vector})”. This is the error message:
„Error in .Call("R_igraph_authority_score", graph, scale, weights,
options, : At arpack.c:944 : ARPACK error, No shifts could be applied during
a cycle of the Implicitly restarted Arnoldi iteration. One possibility is to
increase the size of NCV relative to NEV” I made a google search to
understand what causes the probem, but I didn’t find anything useful.
Maybe I can find something in the arpack manual but I definitely need more time
for that. Here are the details about the subgraph of group 293863: >
g_sub[293863]$`293863`IGRAPH U-W- 4 3 --+ attr: weight (e/n)+ edges:[1] 1--2
1--3 1--4 > E(g_sub[[293863]])$weight[1] 270 5677 3032 I don’t see why
the authority_score function can’t run on that kind of graph (this is a
classical star schemed graph and I think there are many of them because there
are about 440 000 clusters/subgraphs)I hope you can send us some kind of
solution for this problem. Thanks in advance. Best regards,Adam Sohonyai_______________________________________________
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