clustering_w {tnet} | R Documentation |
Generalised clusering coefficient
Description
This function calculates the generalised clusering coefficient as proposed by Opsahl, T., Panzarasa, P., 2009. Clustering in weighted networks. Social Networks 31 (2), 155-163, 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 source-code and Windows-compiled files.
Usage
clustering_w(net, measure = "am")
Arguments
net |
A weighted edgelist |
measure |
The measure-switch control the method used to calculate the value of the triplets. |
Value
Returns the outcome of the equation presented in the paper for the method specific (measure)
Note
version 1.0.0
Author(s)
Tore Opsahl; http://toreopsahl.com
References
Opsahl, T., Panzarasa, P., 2009. Clustering in weighted networks. Social Networks 31 (2), 155-163, doi: 10.1016/j.socnet.2009.02.002
http://toreopsahl.com/2009/04/03/article-clustering-in-weighted-networks/
Examples
## 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)