clusteringCoefAppr
Approximate clustering coefficient for an undirected graph
Approximate clustering coefficient for an undirected graph
 Keywords
 models
Usage
clusteringCoefAppr(g, k=length(nodes(g)), Weighted=FALSE, vW=degree(g))
Arguments
 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
Details
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.
Value

Approximated clustering coefficient for graph
g
.
References
Approximating Clustering Coefficient and Transitivity, T. Schank, D. Wagner, Journal of Graph Algorithms and Applications, Vol. 9, No. 2 (2005).
See Also
clusteringCoef, transitivity, graphGenerator
Examples
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)
Community examples
Looks like there are no examples yet.