# Approximate clustering coefficient for an undirected graph

### Description

Approximate clustering coefficient for an undirected graph

### Usage

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

### Arguments

`g` |
an instance of the |

`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`

.

### Author(s)

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

### 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

1 2 3 4 5 6 7 8 9 | ```
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)
``` |