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.

1 | ```
clustering_w(net, measure = "am")
``` |

`net` |
A weighted edgelist |

`measure` |
The measure-switch 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), 155-163, doi: 10.1016/j.socnet.2009.02.002

http://toreopsahl.com/2009/04/03/article-clustering-in-weighted-networks/

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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