Two-Step Expected Influence

Two-step expected influence (Robinaugh, Millner and McNally 2016) adds to the one-step expected influence of a node the one-step expected influence of its neighbors, each weighted by the signed edge between them: $$EI_2(i) = EI_1(i) + \sum_{j} w_{ij} EI_1(j).$$

Usage

centrality_expected_influence_2(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) follows outgoing edges at both steps, mode = "in" incoming edges and mode = "all" both, with a self-loop counted once. On an undirected network the three modes agree and each edge is counted once. weighted = FALSE gives every edge weight one. 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_2(regulation_net)
#>    Explore       Plan    Monitor      Adapt    Reflect    Discuss Synthesize 
#>     0.9843     2.9500     1.0005     1.3342     0.3105     1.1762     0.8949 
#>   Evaluate     Create      Share 
#>     1.3586     1.6813     1.9896