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

Description

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.

Usage

 `1` ```clustering_tm(net, subsample=1, seed=NULL) ```

Arguments

 `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.

Value

Returns the outcome of the equation presented in the paper

version 1.0.0

Author(s)

Tore Opsahl; http://toreopsahl.com

References

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

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# 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) ```

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