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## [igraph] clustering coefficients for bipartite networks

 From: Simone Gabbriellini Subject: [igraph] clustering coefficients for bipartite networks Date: Sat, 29 Jan 2011 11:46:45 +0100

```Hello List,

I've asked this question some time ago for R and Gabor wrote this code to find
a particular clustering coefficient for bipartite networks (it is also in the
wiki R-recipes).
I now need to implement a python version of it, but I have no clue on how to
implement this use of apply() in python...
I don't think I can define functions on the fly like in R (but maybe I am
wrong), so is it wise to break the code into smaller functions and then call
everything from the principal function (in this example of Gabor's code, called
ccBip)?

ccBip <- function(g) {
names(neib) <- V(g)\$name
proj <- bipartite.projection(g)
lapply(proj, function(x) {
el <- get.edgelist(x)
sapply(V(x)\$name, function(v) {
subs <- el[,1]==v | el[,2]==v
f <- function(un, vn) length(union(un, vn))
vals <- E(x)[subs]\$weight / unlist(mapply(f,
neib[el[subs,1]], neib[el[subs,2]]))
mean(vals)
})
})
}

or maybe are there in python alternative ways to accomplish this task?

best regards,
Simone

```