sample_tree: Sample trees randomly and uniformly

In igraph: Network Analysis and Visualization

Sample trees randomly and uniformly

Description

sample_tree() generates a random with a given number of nodes uniform at random from the set of labelled trees.

Usage

sample_tree(n, directed = FALSE, method = c("lerw", "prufer"))

Arguments

n	The number of nodes in the tree
directed	Whether to create a directed tree. The edges of the tree are oriented away from the root.
method	The algorithm to use to generate the tree. ‘prufer’ samples Prüfer sequences uniformly and then converts the sampled sequence to a tree. ‘lerw’ performs a loop-erased random walk on the complete graph to uniformly sampleits spanning trees. (This is also known as Wilson's algorithm). The default is ‘lerw’. Note that the method based on Prüfer sequences does not support directed trees at the moment.

Details

In other words, the function generates each possible labelled tree with the given number of nodes with the same probability.

Value

A graph object.

Related documentation in the C library

igraph_tree_game().

See Also

Random graph models (games) erdos.renyi.game(), sample_(), sample_bipartite(), sample_chung_lu(), sample_correlated_gnp(), sample_correlated_gnp_pair(), sample_degseq(), sample_dot_product(), sample_fitness(), sample_fitness_pl(), sample_forestfire(), sample_gnm(), sample_gnp(), sample_grg(), sample_growing(), sample_hierarchical_sbm(), sample_islands(), sample_k_regular(), sample_last_cit(), sample_pa(), sample_pa_age(), sample_pref(), sample_sbm(), sample_smallworld(), sample_traits_callaway()

Examples

g <- sample_tree(100, method = "lerw")

igraph documentation built on Oct. 20, 2024, 1:06 a.m.