Re: [igraph] igraph R code to Python

[Simone Gabbriellini]

Tamas,

thanks a lot, lcc.vs is fine for me! ;)
I have uploaded some R code for bipartite networks on the igraph wiki R 
recipes. I am now trying to write a Python version of it, either for my 
research needs and for sake of completing my wiki entry (btw, is there a Python 
recipes page?). 

After retrieving the largest connected component (lcc), I would like to find 
the average shortest paths for top and bottom nodes in it. Is there something 
like:

mean(shortest.paths(lcc, v=V(lcc)[type==FALSE], to=V(lcc)[type==FALSE], mode = 
'all'))
mean(shortest.paths(lcc, v=V(lcc)[type==TRUE], to=V(lcc)[type==TRUE], mode = 
'all'))

I know it is much to ask, but I have also to translate a clustering function 
that Gabor wrote in order to reproduce in python all the clustering measures of 
Latapy, Magnien and Del Vecchio, Social Networks 30 (2008):

ccBip <- function(g) {
if (! "name" %in% list.vertex.attributes(g)) {
  V(g)$name <- seq_len(vcount(g))
}
neib <- get.adjlist(g)
names(neib) <- V(g)$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)
  })
})
}

any help is more than welcome!

best,
Simone