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Calculate Summary of Network Metrics for a Transition Network

Source: R/summary.R
summary.tna.Rd

This function calculates a variety of network metrics for a tna object. It computes key metrics such as node and edge counts, network density, mean distance, strength measures, degree centrality, and reciprocity.

Usage

# S3 method for class 'tna'
summary(object, ...)

Arguments

object

A tna object.

...

Ignored.

Value

A named list containing the following network metrics (invisibly):

  • node_count: The total number of nodes.

  • edge_count: The total number of edges.

  • network_Density: The density of the network.

  • mean_distance: The mean shortest path length.

  • mean_out_strength: The mean out-strength of nodes.

  • sd_out_strength: The standard deviation of out-strength.

  • mean_in_strength: The mean in-strength of nodes.

  • sd_in_strength: The standard deviation of in-strength.

  • mean_out_degree: The mean out-degree of nodes.

  • sd_out_degree: The standard deviation of out-degree.

  • centralization_out_degree: The centralization of out-degree.

  • centralization_in_degree: The centralization of in-degree.

  • reciprocity: The reciprocity of the network.

Details

The function extracts the igraph network and computes the following network metrics:

  • Node count: Total number of nodes in the network.

  • Edge count: Total number of edges in the network.

  • Network density: Proportion of possible edges that are present in the network.

  • Mean distance: The average shortest path length between nodes.

  • Mean and standard deviation of out-strength and in-strength: Measures of the total weight of outgoing and incoming edges for each node.

  • Mean and standard deviation of out-degree: The number of outgoing edges from each node.

  • Centralization of out-degree and in-degree: Measures of how centralized the network is based on the degrees of nodes.

  • Reciprocity: The proportion of edges that are reciprocated (i.e., mutual edges between nodes).

A summary of the metrics is printed to the console.

See also

Basic functions build_model(), hist.group_tna(), hist.tna(), plot.group_tna(), plot.tna(), plot_mosaic(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), prepare_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), print.tna_data(), summary.group_tna(), tna-package

Examples

model <- tna(group_regulation)
summary(model)
#> # A tibble: 13 × 2
#>    metric                         value
#>  * <chr>                          <dbl>
#>  1 Node Count                  9   e+ 0
#>  2 Edge Count                  7.8 e+ 1
#>  3 Network Density             1   e+ 0
#>  4 Mean Distance               4.72e- 2
#>  5 Mean Out-Strength           1   e+ 0
#>  6 SD Out-Strength             8.07e- 1
#>  7 Mean In-Strength            1   e+ 0
#>  8 SD In-Strength              6.80e-17
#>  9 Mean Out-Degree             8.67e+ 0
#> 10 SD Out-Degree               7.07e- 1
#> 11 Centralization (Out-Degree) 1.56e- 2
#> 12 Centralization (In-Degree)  1.56e- 2
#> 13 Reciprocity                 9.86e- 1