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tna (version 0.4.0)

cliques: Identify Cliques in a Transition Network

Description

This function identifies cliques of a specified size in a transition network. It searches for cliques—complete subgraphs where every pair of nodes is connected—of size n in the transition matrix for the specified cluster in the tna object.

Usage

cliques(x, ...)

# S3 method for tna cliques(x, size = 2, threshold = 0, sum_weights = FALSE, ...)

# S3 method for group_tna cliques(x, size = 2, threshold = 0, sum_weights = FALSE, ...)

Value

A tna_cliques object which is a list of two elements:

  • weights is a matrix of the edge weights in the clique.

  • inits is a numeric vector of initial weights for the clique.

If x is a group_tna object, a group_tna_cliques object is returned instead, which is a list or tna_cliques objects.

Arguments

x

A tna or a group_tna object.

...

Ignored.

size

An integer specifying the size of the cliques to identify. Defaults to 2 (dyads).

threshold

A numeric value that sets the minimum edge weight for an edge to be considered in the clique. Edges below this value are ignored. Defaults to 0.

sum_weights

A logical value specifying whether the sum of the weights should be above the threshold instead of individual weights of the directed edges. Defaults to FALSE.

See Also

Cluster-related functions bootstrap(), centralities(), communities(), deprune(), estimate_cs(), group_model(), hist.group_tna(), mmm_stats(), plot.group_tna(), plot.group_tna_centralities(), plot.group_tna_cliques(), plot.group_tna_communities(), plot.group_tna_stability(), plot_compare.group_tna(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.group_tna_bootstrap(), print.group_tna_centralities(), print.group_tna_cliques(), print.group_tna_communities(), print.group_tna_stability(), print.summary.group_tna(), print.summary.group_tna_bootstrap(), prune(), pruning_details(), rename_groups(), reprune(), summary.group_tna(), summary.group_tna_bootstrap()

Examples

Run this code
model <- tna(group_regulation)

# Find  2-cliques (dyads)
cliq <- cliques(model, size = 2)

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