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## Re: [igraph] clustering coefficient in bipartite network

 From: Simone Gabbriellini Subject: Re: [igraph] clustering coefficient in bipartite network Date: Sat, 27 Nov 2010 11:31:04 +0100

```Hi Gabor,

thanks again for this piece of code...

I have to admit I am still trying to figure out what this function does in the
details, because of my lack of R expertise... so many things are unclear but I
am going to figure them out... ;)

the only thing I can say now is that I have this error:

> ccBip(g)
Errore in E(x)[subs]\$weight/mapply(f, neib[el[subs, 1]], neib[el[subs,  :
non-numeric argument transformed in binary operator

my version of igraph is, I guess, the last nightly source,
igraph_nightly_0.6-2030-20100726.tar.gz

best,
Simone

Il giorno 27/nov/2010, alle ore 00.30, Gábor Csárdi ha scritto:

> Hi Simone,
>
> On Fri, Nov 26, 2010 at 12:10 PM, Simone Gabbriellini
>> HI Gabor,
>>
>> thanks very much, yes that is the right direction for me!
>>
>> what I have to reproduce is, according to Latapy:
>>
>> for each bottom node v:
>>        for each bottom node u, 2-dist-neighbor of v:
>>                 find the number of (shared top nodes of u and v) / (total
>> top nodes neighbors of u AND v)
>
> ccBip <- function(g) {
>  if (! "name" %in% list.vertex.attributes(g)) {
>    V(g)\$name <- seq_len(vcount(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 /
>        mapply(f, neib[el[subs,1]], neib[el[subs,2]])
>      mean(vals)
>    })
>  })
> }
>
> I think this does exactly what you want, assuming I understood the
> definition correctly. I have only tested it with igraph 0.6, for which
> the nightly builds are available again at
>
> Please tell me if something is not clear.
>
> Best,
> Gabor
>
>> then to find the local clustering of v, I have to average the list of values
>> obtained.
>>
>> and conversely for top nodes... it's a bit tricky for me with R...
>>
>> thanks a lot,
>> simone
>>
>> Il giorno 26/nov/2010, alle ore 11.34, Gábor Csárdi ha scritto:
>>
>>> Hi Simone,
>>>
>>>
>>> On Thu, Nov 25, 2010 at 5:22 PM, Simone Gabbriellini
>>>> Hello List,
>>>>
>>>> I am trying to reproduce the clustering measures detailed in Latapy et al.
>>>> Social Networks, 30 (2008).
>>>>
>>>> I attempted successfully to reproduce the ccN(G) clustering, which is
>>>> basically an extension for bipartite networks of the global transitivity
>>>> measure.
>>>>
>>>> I am stuck with the cc. measure of clustering coefficient, an extension of
>>>> local transitivity for bipartite network - a reprise of what Borgatti and
>>>> Everett have already suggested in 1997.
>>>>
>>>> I have to find, for each distance-2 neighbors of a node (which are still
>>>> nodes of the same set), how many nodes of the other set they have in
>>>> common.
>>>>
>>>> This is not all of what is needed to implement this measure, but it would
>>>> be a great step for me...
>>>>
>>>> In order to find distance-2 neighbors for each node, I can use a
>>>> partition, as Tamas suggested in a previous thread.
>>>>
>>>> V(g)[type==FALSE]\$neibi<-neighborhood(bipartite.projection(g)[[1]], 1)
>>>>
>>>> V(g)[type==TRUE]\$neibi<-neighborhood(bipartite.projection(g)[[2]], 1)
>>>
>>> it is actually better to use vertex names to be sure that you assign
>>> the second neighbors to the right vertices. While your solution works
>>> if the order of the vertices is kept in the projections, this is not
>>> documented for bipartite.projection, so you cannot take it for
>>> granted.
>>>
>>> Anyway, I think an easier way to get the second neighbors is to simply
>>> subtract the 1-neighborhood from the 1-2-neighborhood, this works for
>>> bipartite graphs.
>>>
>>> nei12 <- neighborhood(g, 2)
>>> nei1 <- neighborhood(g, 1)
>>> nei2 <- mapply(setdiff, nei12, nei1)
>>>
>>>> While in order to find neighbors in the bipartite, I can simply use:
>>>>
>>>> V(g)\$nei<-neighborhood(g, 1)
>>>>
>>>> now, how can I confront a node with every nodes listed in its neibi
>>>> attribute in order to find if there are duplicates in each nei attributes?
>>>> this is the hardest part I cannot solve.
>>>
>>> If you have two numeric or character vectors, 'v1' and 'v2', then
>>> 'intersection(v1, v2)' treats them as sets and gives a vector that is
>>> their intersection. Is this what you need?
>>>
>>> G.
>>>
>>>> any help more than welcome!
>>>>
>>>> Simone
>>>> _______________________________________________
>>>> igraph-help mailing list
>>>> http://lists.nongnu.org/mailman/listinfo/igraph-help
>>>>
>>>
>>>
>>>
>>> --
>>> Gabor Csardi <address@hidden>     UNIL DGM
>>>
>>> _______________________________________________
>>> igraph-help mailing list
>>> http://lists.nongnu.org/mailman/listinfo/igraph-help
>>
>>
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>
>
>
> --
> Gabor Csardi <address@hidden>     UNIL DGM
>
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> igraph-help mailing list