They can be regarded as incidence matrices rather than adjacency
matrices and in that case it follows:
library(igraph)
# incidence matrix to canonical edge list
inc2canel <- function(m) {
g <- graph.incidence(m)
cp <- canonical.permutation(g)
can <- permute.vertices(g, cp$labeling)
el <- get.edgelist(can)
el[ order(el[,1], el[,2]), ]
}
# test it out
m1 <- matrix(c(0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1,
0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0,
0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1), 7)
m2 <- m1[7:1, 8:1]
m3 <- m2; m3[1, ] <- 1
identical(inc2canel(m1), inc2canel(m2)) # TRUE
identical(inc2canel(m1), inc2canel(m3)) # FALSE
On Sun, Aug 23, 2009 at 6:09 PM, Steve
Lianoglou<mailinglist.honey...@gmail.com> wrote:
Hi,
On Sun, Aug 23, 2009 at 4:14 PM, Michael Kogan<michael.ko...@gmx.net> wrote:
Thanks for all the replies!
Steve: I don't know whether my suggestion is a good one. I'm quite new to
programming, have absolutely no experience and this was the only one I could
think of. :-) I'm not sure whether I'm able to put your tips into practice,
unfortunately I had no time for much reading today but I'll dive into it
tomorrow.
Ok, yeah. I'm not sure what the best way to do this myself, I would at
first see if one could reduce these matrices by some principled manner
and then do a comparison, which might jump to:
Ted: Wow, that's heavy reading. In fact the matrices that I need to compare
are incidence matrices so I suppose it's exactly the thing I need, but I
don't know if I have the basics knowledge to understand this paper within
the next months.
Ted's sol'n. I haven't read the paper, but its title gives me an idea.
Perhaps you can assume the two matrices you are comparing are
adjacency matrices for a graph then use the igraph library to do a
graph isomorphism test between the two graphs represented by your
adjacency matrices and see if they are the same.
This is probably not the most efficient (computationally) way to do
it, but it might be the quickest way out coding-wise.
I see your original example isn't using square matrices, and an
adjacency matrix has to be square. Maybe you can pad your matrices
with zero rows or columns (depending on what's deficient) as an easy
way out.
Just an idea.
Of course, if David's solution is what you need, then no need to
bother with any of this.
-steve
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
