clusteringCoef {RBGL} | R Documentation |
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 <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).
clusteringCoefAppr, transitivity, graphGenerator
g <- fromGXL(file(system.file("XML/conn.gxl",package="RBGL"))) cc <- clusteringCoef(g) ccw1 <- clusteringCoef(g, Weighted=TRUE) vW <- c(1, 1, 1, 1, 1,1, 1, 1) ccw2 <- clusteringCoef(g, Weighted=TRUE, vW)