Bridge centrality

Computes bridge centrality for an undirected weighted network: how strongly each node connects to communities other than its own. You supply the community membership; psychnets does not detect it.

Usage

net_bridge(x, communities, normalize = FALSE, labels = NULL)

Arguments

x

A psychnet object or a square weighted adjacency matrix.

communities

Community membership, one entry per node: a vector aligned to the node order, or a named vector / list keyed by node label.

normalize

If TRUE, divide each metric by the number of available other-community nodes (comparable across differently sized networks). Default FALSE.

labels

Optional node labels (used when x is a bare matrix).

Value

A tidy data.frame (class psychnet_bridge), one row per node, with columns node, community, bridge_strength, bridge_betweenness, bridge_closeness, bridge_ei1, bridge_ei2. Visualise with plot.psychnet_bridge().

References

Jones, P. J., Ma, R., & McNally, R. J. (2021). Bridge centrality. Multivariate Behavioral Research, 56(2), 353-367.

Examples

S <- 0.3^abs(outer(1:6, 1:6, "-"))
fit <- ebic_glasso(cor_matrix = S, n = 400)
net_bridge(fit, communities = c(1, 1, 1, 2, 2, 2))
#>   node community bridge_strength bridge_betweenness bridge_closeness bridge_ei1
#> 1   V1         1       0.0000000                  0       0.06918582  0.0000000
#> 2   V2         1       0.0000000                  3       0.09139545  0.0000000
#> 3   V3         1       0.2729255                  6       0.13741057  0.2729255
#> 4   V4         2       0.2729255                  6       0.13741057  0.2729255
#> 5   V5         2       0.0000000                  3       0.09139545  0.0000000
#> 6   V6         2       0.0000000                  0       0.06918582  0.0000000
#>   bridge_ei2
#> 1 0.00000000
#> 2 0.07448834
#> 3 0.34741385
#> 4 0.34741385
#> 5 0.07448834
#> 6 0.00000000