clustering_w: Generalised clusering coefficient In tnet: Software for Analysis of Weighted, Two-Mode, and Longitudinal Networks

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

 `1` ```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. am implies the arithmetic mean method (default) gm implies the geometric mean method mi implies the minimum method ma implies the maximum method bi implies the binary measure This can be c("am", "gm", "mi", "ma", "bi") to calculate all.

Value

Returns the outcome of the equation presented in the paper for the method specific (measure)

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

 ```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) ```

tnet documentation built on May 30, 2017, 4:31 a.m.