One-Step Expected Influence

One-step expected influence (Robinaugh, Millner and McNally 2016) sums the signed weights of a node's edges: $$EI_1(i) = \sum_{j} w_{ij}.$$ Negative edges lower the score, which makes the measure suited to partial-correlation and other signed networks.

Usage

centrality_expected_influence_1(x, mode = "out", ...)

Arguments

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

Value

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

Details

mode = "out" (the default here) sums the outgoing weights, mode = "in" the incoming weights and mode = "all" both, with a self-loop counted once. On an undirected network the three modes agree and each edge is counted once. With weighted = FALSE, or on an unweighted input, the score is the degree in the chosen mode. When the network has a negative edge, normalized = TRUE divides by the largest absolute score and keeps the sign.

References

Robinaugh, D. J., Millner, A. J., & McNally, R. J. (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology, 125(6), 747-757. doi:10.1037/abn0000181 .

Examples

centrality_expected_influence_1(regulation_net)
#>    Explore       Plan    Monitor      Adapt    Reflect    Discuss Synthesize 
#>       0.62       1.58       0.53       0.79       0.20       0.79       0.60 
#>   Evaluate     Create      Share 
#>       0.83       0.93       1.09