Expected Centrality

Expected centrality is the sum of the degrees of a node's neighbors: $$E(v) = \sum_{u \in N(v)} k_u.$$

Usage

centrality_expected(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 degrees and the neighbor set. On a directed network mode = "all" uses total degrees and the undirected neighbor set. Adding the node's own degree gives the "kandhway_kuri" form of centrality_diffusion.

See also

centrality_diffusion, centrality_neighborhood_connectivity, centrality.

Examples

centrality_expected(regulation_net)
#>    Explore       Plan    Monitor      Adapt    Reflect    Discuss Synthesize 
#>         30         35         41         34         28         32         27 
#>   Evaluate     Create      Share 
#>         34         37         34