This function calculates the generalised clusering coefficient as proposed by Opsahl, T., Panzarasa, P., 2009. Clustering in weighted networks. Social Networks 31 (2), 155163, doi: 10.1016/j.socnet.2009.02.002
Note: If you are having problems with this function (i.e., run out of memory or it being slow for simulations), there is a quicker and much more memory efficient c++ function. However, this function is not fully integrated in R, and requires a few extra steps. Send me an email to get the sourcecode and Windowscompiled files.
1  clustering_w(net, measure = "am")

net 
A weighted edgelist 
measure 
The measureswitch control the method used to calculate the value of the triplets. 
Returns the outcome of the equation presented in the paper for the method specific (measure)
version 1.0.0
Tore Opsahl; http://toreopsahl.com
Opsahl, T., Panzarasa, P., 2009. Clustering in weighted networks. Social Networks 31 (2), 155163, doi: 10.1016/j.socnet.2009.02.002
http://toreopsahl.com/2009/04/03/articleclusteringinweightednetworks/
1 2 3 4 5 6 7  ## Generate a random graph
#density: 300/(100*99)=0.03030303;
#this should be average from random samples
rg < rg_w(nodes=100,arcs=300,weights=1:10)
## Run clustering function
clustering_w(rg)

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