Description Usage Arguments Details Value Author(s) References See Also Examples

Calculate clustering coefficient for an undirected graph

1 | ```
clusteringCoef(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 |

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 [email protected]

Approximating Clustering Coefficient and Transitivity, T. Schank, D. Wagner, Journal of Graph Algorithms and Applications, Vol. 9, No. 2 (2005).

clusteringCoefAppr, transitivity, graphGenerator

1 2 3 4 5 6 7 | ```
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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