sample_growing: Growing random graph generation

In igraph: Network Analysis and Visualization

Growing random graph generation

Description

This function creates a random graph by simulating its stochastic evolution.

Usage

sample_growing(n, m = 1, ..., directed = TRUE, citation = FALSE)

growing(...)

Arguments

n	Numeric constant, number of vertices in the graph.
m	Numeric constant, number of edges added in each time step.
...	Passed to sample_growing().
directed	Logical, whether to create a directed graph.
citation	Logical. If TRUE a citation graph is created, i.e. in each time step the added edges are originating from the new vertex.

Details

This is discrete time step model, in each time step a new vertex is added to the graph and m new edges are created. If citation is FALSE these edges are connecting two uniformly randomly chosen vertices, otherwise the edges are connecting new vertex to uniformly randomly chosen old vertices.

Value

A new graph object.

Related documentation in the C library

igraph_growing_random_game().

Author(s)

Gabor Csardi [e-mail]

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_hierarchical_sbm(), sample_islands(), sample_k_regular(), sample_last_cit(), sample_pa(), sample_pa_age(), sample_pref(), sample_sbm(), sample_smallworld(), sample_traits_callaway(), sample_tree()

Examples

g <- sample_growing(500, citation = FALSE)
g2 <- sample_growing(500, citation = TRUE)

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