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

Usage

clustering_w(net, measure = "mi")

Arguments

net A weighted edgelist
measure The measure-switch control the method used to calculate the value of the triplets.
am implies the arithmetic mean method
gm implies the geometric mean method
mi implies the minimum method
ma implies the maximum method
This can be c("am", "gm", "mi", "ma") to calculate all.

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,max.weight=10)

## Run clustering function
clustering_w(rg)


[Package tnet version 0.1.2 Index]