# clusteringCoefAppr

0th

Percentile

##### 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.

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).

clusteringCoef, transitivity, graphGenerator

##### Aliases
• clusteringCoefAppr
##### 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)

Documentation reproduced from package RBGL, version 1.48.1, License: Artistic-2.0

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