This function creates a random graph by simulating its stochastic evolution.
sample_growing(n, m = 1, ..., directed = TRUE, citation = FALSE)
growing(...)Numeric constant, number of vertices in the graph.
Numeric constant, number of edges added in each time step.
Passed to sample_growing().
Logical, whether to create a directed graph.
Logical. If TRUE a citation graph is created, i.e. in
each time step the added edges are originating from the new vertex.
A new graph object.
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.
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()
g <- sample_growing(500, citation = FALSE)
g2 <- sample_growing(500, citation = TRUE)