Density of Maximum Neighborhood Component

The density of maximum neighborhood component (Lin et al. 2008) looks at the subnetwork induced by the neighbors of a node, the node itself left out, and takes its largest connected component with $E$ edges and $N$ nodes: $$DMNC(v) = \frac{E}{N^{\varepsilon}}.$$ A node without neighbors scores 0.

Usage

centrality_dmnc(x, mode = "all", dmnc_epsilon = 1.7, ...)

Arguments

x: Network input accepted by centrality.
mode: For directed networks: "all" (default), "out" or "in".
dmnc_epsilon: Exponent $\varepsilon$. Default 1.7, the value Lin et al. (2008) recommend. The centiserve package uses 1.67.
...: 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, and mode sets the neighbor set. On an undirected network the result follows this definition. On a directed network the component is a strongly connected component of the induced subnetwork, $E$ counts its directed edges, and a reciprocated neighbor enters the subnetwork once. When several components share the largest size, $E$ counts the edges among all of their nodes. A dmnc_epsilon that is not a single positive finite number raises a cograph_bad_parameter error.

References

Lin, C.-Y., Chin, C.-H., Wu, H.-H., Chen, S.-H., Ho, C.-W., & Ko, M.-T. (2008). Hubba: Hub objects analyzer, a framework of interactome hubs identification for network biology. Nucleic Acids Research, 36(suppl 2), W438-W443. doi:10.1093/nar/gkn257 .

Examples

centrality_dmnc(regulation_net, directed = FALSE)
#>    Explore       Plan    Monitor      Adapt    Reflect    Discuss Synthesize 
#>  0.3241313  0.3328441  0.4024631  0.2377458  0.3089754  0.2593051  0.2841969 
#>   Evaluate     Create      Share 
#>  0.3241313  0.3803933  0.3889576