clusteringCoef
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).
See Also
clusteringCoefAppr, transitivity, graphGenerator
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)
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