clusteringCoefAppr: Approximate clustering coefficient for an undirected graph
In RBGL: An interface to the BOOST graph library

Description Usage Arguments Details Value Author(s) References See Also Examples

Approximate clustering coefficient for an undirected graph

1	clusteringCoefAppr(g, k=length(nodes(g)), Weighted=FALSE, vW=degree(g))

`g`	an instance of the `graph` class
`Weighted`	calculate weighted clustering coefficient or not
`vW`	vertex weights to use when calculating weighted clustering coefficient
`k`	parameter controls total expected runtime

It is quite expensive to compute cluster coefficient and transitivity exactly for a large graph by computing the number of triangles in the graph. Instead, clusteringCoefAppr samples triples with appropriate probability, returns the ratio between the number of existing edges and the number of samples.

MORE ABOUT CHOICE OF K.

See reference for more details.

Approximated clustering coefficient for graph g.

Li Long <li.long@isb-sib.ch>

Approximating Clustering Coefficient and Transitivity, T. Schank, D. Wagner, Journal of Graph Algorithms and Applications, Vol. 9, No. 2 (2005).

clusteringCoef, transitivity, graphGenerator

con <- file(system.file("XML/conn.gxl",package="RBGL"))
g <- fromGXL(con)
close(con)

k = length(nodes(g))
cc <- clusteringCoefAppr(g, k)
ccw1 <- clusteringCoefAppr(g, k, Weighted=TRUE)
vW  <- c(1, 1, 1, 1, 1,1, 1, 1)
ccw2 <- clusteringCoefAppr(g, k, Weighted=TRUE, vW)