Hi Thomas,
1) instead of length(degree(g)), just use vcount(g)
2) neighbors(g, node1) can be queried outside the interval while() loop and
then stored in a temporary variable because it won't change during the lifetime
of the inner loop
3) if you are sampling from the range 1:n, use sample.int() instead of sample()
4) instead of rbinom(1, 1, p), use runif(1)<p -- it is probably faster
I think this should make things faster -- let me know if it is still too slow.
--
T.
On 24 Oct 2013, at 15:10, Thomas <address@hidden> wrote:
I'm creating a sample of nodes according to the random walk procedure
described in Section 3.3.3 of:
http://www.stat.cmu.edu/~fienberg/Stat36-835/Leskovec-sampling-kdd06.pdf
The following R code samples no less than 300 nodes, although it might sample
the same node twice but it runs really slowly. Does anyone know why it might
be going so slow? Is there any better way to do this?
Thank you,
Thomas
#Random Walk Sample of nodes from network g
#Read graph g in as UNDIRECTED
A <- sample(1:length(degree(g)), 1)
oput <- c()
oput <- c(oput, A)
flag <- FALSE
count <- 1
while(count <= 300)
{
node1 <- A
while(flag==FALSE)
{
node2 <- sample(neighbors(g,node1),1)
oput <- c(oput, node2)
count <- count + 1
node1 <- node2
if(rbinom(1,1,0.15)==1){flag=TRUE}
}#end of while flag loop
}#end of while count loop
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