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
graphclass - 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
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