`sample_last_cit()`

creates a graph, where vertices age, and
gain new connections based on how long ago their last citation
happened.

## Usage

```
sample_last_cit(
n,
edges = 1,
agebins = n/7100,
pref = (1:(agebins + 1))^-3,
directed = TRUE
)
last_cit(...)
sample_cit_types(
n,
edges = 1,
types = rep(0, n),
pref = rep(1, length(types)),
directed = TRUE,
attr = TRUE
)
cit_types(...)
sample_cit_cit_types(
n,
edges = 1,
types = rep(0, n),
pref = matrix(1, nrow = length(types), ncol = length(types)),
directed = TRUE,
attr = TRUE
)
cit_cit_types(...)
```

## Arguments

- n
Number of vertices.

- edges
Number of edges per step.

- agebins
Number of aging bins.

- pref
Vector (

`sample_last_cit()`

and`sample_cit_types()`

or matrix (`sample_cit_cit_types()`

) giving the (unnormalized) citation probabilities for the different vertex types.- directed
Logical scalar, whether to generate directed networks.

- ...
Passed to the actual constructor.

- types
Vector of length ‘

`n`

’, the types of the vertices. Types are numbered from zero.- attr
Logical scalar, whether to add the vertex types to the generated graph as a vertex attribute called ‘

`type`

’.

## Details

`sample_cit_cit_types()`

is a stochastic block model where the
graph is growing.

`sample_cit_types()`

is similarly a growing stochastic block model,
but the probability of an edge depends on the (potentially) cited
vertex only.

## See also

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

,
`sample_bipartite()`

,
`sample_correlated_gnp_pair()`

,
`sample_correlated_gnp()`

,
`sample_degseq()`

,
`sample_dot_product()`

,
`sample_fitness_pl()`

,
`sample_fitness()`

,
`sample_forestfire()`

,
`sample_gnm()`

,
`sample_gnp()`

,
`sample_grg()`

,
`sample_growing()`

,
`sample_hierarchical_sbm()`

,
`sample_islands()`

,
`sample_k_regular()`

,
`sample_pa_age()`

,
`sample_pa()`

,
`sample_pref()`

,
`sample_sbm()`

,
`sample_smallworld()`

,
`sample_traits_callaway()`

,
`sample_tree()`

,
`sample_()`

## Author

Gabor Csardi csardi.gabor@gmail.com