clusteringCoef {RBGL}R Documentation

Calculate clustering coefficient for an undirected graph


Calculate clustering coefficient for an undirected graph


clusteringCoef(g, Weighted=FALSE, vW=degree(g))


g an instance of the graph class
Weighted calculate weighted clustering coefficient or not
vW vertex weights to use when calculating weighted clustering coefficient


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'.


Clustering coefficient for graph G.


Li Long


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


con <- file(system.file("XML/conn.gxl",package="RBGL"))
g <- fromGXL(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)

[Package RBGL version 1.20.0 Index]