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

 From: Simone Gabbriellini Subject: Re: [igraph] clustering coefficient in bipartite network Date: Fri, 26 Nov 2010 12:10:38 +0100

```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)

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
>
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