clustering_w_barrat {tnet} | R Documentation |
This function calculates Barrat et al. (2004) generalised local clusering coefficient.
See http://toreopsahl.com/2009/01/23/weighted-local-clustering-coefficient/ for a detailed description. By default it measure the triplet value as the average of the two ties; however it can also define it differently. See the blog post.
clustering_w_barrat(net, measure = "am")
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. |
Returns a data.frame with at least two columns: the first containts the node ids of all the nodes in the edgelist, and the remaining ones containt the corresponding clustering scores.
version 1.0.0
Tore Opsahl; http://toreopsahl.com
Barrat, A., Barthelemy, M., Pastor-Satorras, R., Vespignani, A., 2004. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences 101 (11), 3747-3752. arXiv:cond-mat/0311416
http://toreopsahl.com/2009/01/23/weighted-local-clustering-coefficient/
## 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,directed=FALSE) ## Run clustering function clustering_w_barrat(rg)