Approximate clustering coefficient for an undirected graph
Approximate clustering coefficient for an undirected graph
clusteringCoefAppr(g, k=length(nodes(g)), Weighted=FALSE, vW=degree(g))
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 |
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.
Approximated clustering coefficient for graph g.
Li Long <li.long@isb-sib.ch>
Approximating Clustering Coefficient and Transitivity, T. Schank, D. Wagner, Journal of Graph Algorithms and Applications, Vol. 9, No. 2 (2005).
clusteringCoef, transitivity, graphGenerator
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.