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Build and Visualize a Network with Edge Betweenness

Source: R/betweenness.R
betweenness_network.Rd

This function builds a network from a transition matrix in a tna object and computes edge betweenness for the network.

Usage

betweenness_network(x, directed = TRUE, invert = TRUE)

# S3 method for class 'tna'
betweenness_network(x, directed = TRUE, invert = TRUE)

Arguments

x

A tna object.

directed

A logical value. If TRUE, the network is considered directed.

invert

A logical value indicating whether the weights should be inverted for distance-based measures. The default is TRUE.

Value

A tna object where the edge weights are edge betweenness values.

See also

Centrality measure functions centralities(), plot.group_tna_centralities(), plot.tna_centralities(), print.group_tna_centralities(), print.tna_centralities()

Examples

model <- tna(group_regulation)
betweenness_network(model)
#> State Labels : 
#> 
#>    adapt, cohesion, consensus, coregulate, discuss, emotion, monitor, plan, synthesis 
#> 
#> Edge Betweenness Matrix :
#> 
#>            adapt cohesion consensus coregulate discuss emotion monitor plan
#> adapt          0        2         6          0       0       1       0    0
#> cohesion       0        0         7          0       0       1       0    0
#> consensus      0        0         0          8      15       0       0   15
#> coregulate     0        0         0          0       4       2       1    1
#> discuss        0        0         7          0       0       2       0    0
#> emotion        0        6         7          0       0       0       0    0
#> monitor        0        0         0          0       5       2       0    1
#> plan           0        0         5          0       0       5       7    0
#> synthesis      9        0         6          0       0       0       0    0
#>            synthesis
#> adapt              0
#> cohesion           0
#> consensus          0
#> coregulate         0
#> discuss           15
#> emotion            0
#> monitor            0
#> plan               0
#> synthesis          0
#> 
#> Initial Probabilities : 
#> 
#>      adapt   cohesion  consensus coregulate    discuss    emotion    monitor 
#>     0.0115     0.0605     0.2140     0.0190     0.1755     0.1515     0.1440 
#>       plan  synthesis 
#>     0.2045     0.0195