`sample_last_cit`

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

```
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(...)

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`

’.

A new graph.

`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 (potentiall) cited
vertex only.