ClusterRank

ClusterRank (Chen et al. 2013) combines the local clustering coefficient $c_v$ of a node with the degrees of its neighbors: $$CR(v) = c_v \sum_{u \in N(v)} (k_u + 1).$$

Usage

centrality_clusterrank(x, mode = "all", ...)

Arguments

x: Network input accepted by centrality.
mode: For directed networks: "all" (default), "out" or "in".
...: Further arguments to centrality, such as normalized.

Value

A named numeric vector with one score per node, in input node order.

Details

Edge weights are ignored. mode sets both the neighbor set and the degrees, and on a directed network a reciprocated neighbor enters the sum twice under mode = "all". The clustering coefficient ignores edge direction. A node with fewer than two neighbors has no clustering coefficient and returns NaN. Chen et al. weight the sum by $10^{-c_v}$, and the measure here multiplies by $c_v$ itself, as the centiserve package does.

References

Chen, D.-B., Gao, H., Lu, L., & Zhou, T. (2013). Identifying influential nodes in large-scale directed networks: The role of clustering. PLoS ONE, 8(10), e77455. doi:10.1371/journal.pone.0077455 .

Examples

centrality_clusterrank(regulation_net)
#>    Explore       Plan    Monitor      Adapt    Reflect    Discuss Synthesize 
#>   21.00000   22.40000   29.33333   13.33333   12.00000   14.80000   15.50000 
#>   Evaluate     Create      Share 
#>   19.50000   27.73333   28.20000