clustering_tm: Redefined clusering coefficient for two-mode networks
In tnet: Weighted, Two-Mode, and Longitudinal Networks Analysis

Description Usage Arguments Value Note Author(s) References Examples

This function calculates the two-mode clusering coefficient as proposed by Opsahl, T., 2010. Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. arXiv:1006.0887.
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_tm(net, subsample=1, seed=NULL)

`net`	A binary or weighted two-mode edgelist
`subsample`	Whether a only a subset of 4-paths should we used when calculating the measure. This is particularly useful when running out of memory analysing large networks. If it is set to 1, all the 4-paths are analysed. If it set to a value below one, this is roughly the proportion of 4-paths that will be analysed. If it is set to an interger greater than 1, this number of ties that form the first part of a 4-path that will be analysed. Note: The c++ functions are better as they analyse the full network.
`seed`	If a subset of 4-paths is analysed, by setting this parameter, the results are reproducable.

Returns the outcome of the equation presented in the paper

version 1.0.0

Tore Opsahl; http://toreopsahl.com

Tore Opsahl. Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. arXiv:1006.0887

# Weighted two-mode network
net <- cbind(
i=c(1,1,2,2,2,3,3,4,5,5,6),
p=c(1,2,1,3,4,2,3,4,3,5,5),
w=c(3,5,6,1,2,6,2,1,3,1,2))

## Run binary clustering function
clustering_tm(net[,1:2])

## Run weighted clustering function
clustering_tm(net)