Re: [igraph] clustering coefficient in bipartite network

[Simone Gabbriellini]

Hi Gabor,

just installed this latest, but got same error... 

no need to rewrite for 0.5! 

best,
Simone

Il giorno 27/nov/2010, alle ore 11.37, Gábor Csárdi ha scritto:

> Hi Simone,
> 
> the latest is from last week,
> http://code.google.com/p/igraph/downloads/detail?name=igraph_nightly_0.6-2221-20101122.tar.gz
> 
> But be careful, because this one has the one-based indexing already.
> Alternatively I can rewrite the code to work with 0.5.
> 
> Gabor
> 
> On Sat, Nov 27, 2010 at 11:31 AM, Simone Gabbriellini
> <address@hidden> wrote:
>> 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]$weight/mapply(f, neib[el[subs, 1]], neib[el[subs,  :
>>        non-numeric argument transformed in binary operator
>> 
>> my version of igraph is, I guess, the last nightly source, 
>> igraph_nightly_0.6-2030-20100726.tar.gz
>> 
>> best,
>> Simone
>> 
>> 
>> Il giorno 27/nov/2010, alle ore 00.30, Gábor Csárdi ha scritto:
>> 
>>> Hi Simone,
>>> 
>>> On Fri, Nov 26, 2010 at 12:10 PM, Simone Gabbriellini
>>> <address@hidden> wrote:
>>>> HI Gabor,
>>>> 
>>>> thanks very much, yes that is the right direction for me!
>>>> 
>>>> what I have to reproduce is, according to Latapy:
>>>> 
>>>> for each bottom node v:
>>>>        for each bottom node u, 2-dist-neighbor of v:
>>>>                 find the number of (shared top nodes of u and v) / (total 
>>>> top nodes neighbors of u AND v)
>>> 
>>> ccBip <- function(g) {
>>>  if (! "name" %in% list.vertex.attributes(g)) {
>>>    V(g)$name <- seq_len(vcount(g))
>>>  }
>>>  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]$weight /
>>>        mapply(f, neib[el[subs,1]], neib[el[subs,2]])
>>>      mean(vals)
>>>    })
>>>  })
>>> }
>>> 
>>> I think this does exactly what you want, assuming I understood the
>>> definition correctly. I have only tested it with igraph 0.6, for which
>>> the nightly builds are available again at
>>> http://code.google.com/p/igraph/downloads/list
>>> 
>>> Please tell me if something is not clear.
>>> 
>>> Best,
>>> Gabor
>>> 
>>>> then to find the local clustering of v, I have to average the list of 
>>>> values obtained.
>>>> 
>>>> and conversely for top nodes... it's a bit tricky for me with R...
>>>> 
>>>> thanks a lot,
>>>> simone
>>>> 
>>>> Il giorno 26/nov/2010, alle ore 11.34, Gábor Csárdi ha scritto:
>>>> 
>>>>> Hi Simone,
>>>>> 
>>>>> a couple of comments.
>>>>> 
>>>>> On Thu, Nov 25, 2010 at 5:22 PM, Simone Gabbriellini
>>>>> <address@hidden> wrote:
>>>>>> Hello List,
>>>>>> 
>>>>>> I am trying to reproduce the clustering measures detailed in Latapy et 
>>>>>> al. Social Networks, 30 (2008).
>>>>>> 
>>>>>> I attempted successfully to reproduce the ccN(G) clustering, which is 
>>>>>> basically an extension for bipartite networks of the global transitivity 
>>>>>> measure.
>>>>>> 
>>>>>> I am stuck with the cc. measure of clustering coefficient, an extension 
>>>>>> of local transitivity for bipartite network - a reprise of what Borgatti 
>>>>>> and Everett have already suggested in 1997.
>>>>>> 
>>>>>> I have to find, for each distance-2 neighbors of a node (which are still 
>>>>>> nodes of the same set), how many nodes of the other set they have in 
>>>>>> common.
>>>>>> 
>>>>>> This is not all of what is needed to implement this measure, but it 
>>>>>> would be a great step for me...
>>>>>> 
>>>>>> In order to find distance-2 neighbors for each node, I can use a 
>>>>>> partition, as Tamas suggested in a previous thread.
>>>>>> 
>>>>>> V(g)[type==FALSE]$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!
>>>>>> 
>>>>>> thanks in advance,
>>>>>> Simone
>>>>>> _______________________________________________
>>>>>> igraph-help mailing list
>>>>>> address@hidden
>>>>>> http://lists.nongnu.org/mailman/listinfo/igraph-help
>>>>>> 
>>>>> 
>>>>> 
>>>>> 
>>>>> --
>>>>> Gabor Csardi <address@hidden>     UNIL DGM
>>>>> 
>>>>> _______________________________________________
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>>>> 
>>>> 
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>>> 
>>> 
>>> 
>>> --
>>> Gabor Csardi <address@hidden>     UNIL DGM
>>> 
>>> _______________________________________________
>>> igraph-help mailing list
>>> address@hidden
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>> 
>> 
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> 
> 
> 
> -- 
> Gabor Csardi <address@hidden>     UNIL DGM
> 
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