# clusteringCoef

0th

Percentile

##### Calculate clustering coefficient for an undirected graph

Calculate clustering coefficient for an undirected graph

Keywords
models
##### Usage
clusteringCoef(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
##### Details

For an undirected graph G, let delta(v) be the number of triangles with v as a node, let tau(v) be the number of triples, i.e., paths of length 2 with v as the center node.

Let V' be the set of nodes with degree at least 2.

Define clustering coefficient for v, c(v) = (delta(v) / tau(v)).

Define clustering coefficient for G, C(G) = sum(c(v)) / |V'|, for all v in V'.

Define weighted clustering coefficient for g, Cw(G) = sum(w(v) * c(v)) / sum(w(v)), for all v in V'.

##### Value

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).

clusteringCoefAppr, transitivity, graphGenerator

##### Aliases
• clusteringCoef
##### Examples
con <- file(system.file("XML/conn.gxl",package="RBGL"))
g <- fromGXL(con)
close(con)
cc <- clusteringCoef(g)
ccw1 <- clusteringCoef(g, Weighted=TRUE)
vW  <- c(1, 1, 1, 1, 1,1, 1, 1)
ccw2 <- clusteringCoef(g, Weighted=TRUE, vW)

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

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