Approximate clustering coefficient for an undirected graph

Description

Approximate clustering coefficient for an undirected graph

Usage

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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.

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

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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)

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