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

## Arguments

- n
Numeric constant, number of vertices in the graph.

- m
Numeric constant, number of edges added in each time step.

- 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.- ...
Passed to

`sample_growing()`

.

## 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.

## See also

Random graph models (games)
`erdos.renyi.game()`

,
`sample_()`

,
`sample_bipartite()`

,
`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()`

## Author

Gabor Csardi csardi.gabor@gmail.com