# sample_last_cit

##### Random citation graphs

`sample_last_cit`

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

- Keywords
- graphs

##### 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 (potentiall) cited
vertex only.

##### Value

A new graph.

*Documentation reproduced from package igraph, version 1.0.1, License: GPL (>= 2)*