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## Re: [igraph] clustering coefficients for bipartite networks

 From: Simone Gabbriellini Subject: Re: [igraph] clustering coefficients for bipartite networks Date: Tue, 1 Feb 2011 17:40:28 +0100

```I guess I have to refine this description: I need to find, for a node u, the
average of clustering values that u has with all the neighbors v which u share
some neighbors with, where the clustering value is computed as the number of
same neighbors between u and v divided by the total number of unique neighbors
of u and v.

is this something appreciable in that direction?

[ ( len( set( g.neighbors(u) ) & set( g.neighbors(v) ) ) / len( list( set(
g.neighbors(u) + g.neighbors(v) ) ) ) ) for u in g.vs(type=0) for v in
g.vs(type=0)]

best,
simone

Il giorno 01/feb/2011, alle ore 16.08, Simone Gabbriellini ha scritto:

> I am really facing difficulties in translating the code from R... Maybe, it
> would be better to start from scratch in python.
>
> I am trying to find, for every pair (u,v) of nodes of the same set, how many
> neighbors of the other sets they have in common divided by the  sum of unique
> neighbors of u and v.
>
> is there a simple way to accomplish this task?
>
> If I am right, this should produce, for every node, a list of values as long
> as the number of pairs of nodes in a set. I then can use a running mean to
> calculate the average value of the list.
>
> best,
> Simone
>
> Il giorno 29/gen/2011, alle ore 11.50, Tamás Nepusz ha scritto:
>
>>> I don't think I can define functions on the fly like in R (but maybe I am
>>> wrong)
>> You can, see the lambda keyword:
>>
>> http://diveintopython.org/power_of_introspection/lambda_functions.html
>>
>> You can also define functions within functions, so if your auxiliary
>> function is longer (but you won't use it from anywhere else), you can define
>> it inside your original function.
>>
>> I don't know exactly what the difference is between the different *apply
>> functions in R (lapply, sapply, apply), but I believe that Python's map()
>> function does something similar. But even better, use list comprehensions if
>> possible:
>>
>> http://docs.python.org/tutorial/datastructures.html#list-comprehensions
>>
>> --
>> T.
>>
>>
>> _______________________________________________
>> igraph-help mailing list