To an existing graph object, add a graph built according to the Erdos-Renyi G(n, p) model, which uses a constant probability when creating edges.

## Usage

```
add_gnp_graph(
graph,
n,
p,
loops = FALSE,
type = NULL,
label = TRUE,
rel = NULL,
node_aes = NULL,
edge_aes = NULL,
node_data = NULL,
edge_data = NULL,
set_seed = NULL
)
```

## Arguments

- graph
A graph object of class

`dgr_graph`

.- n
The number of nodes comprising the generated graph.

- p
The probability of creating an edge between two arbitrary nodes.

- loops
A logical value (default is

`FALSE`

) that governs whether loops are allowed to be created.- type
An optional string that describes the entity type for all the nodes to be added.

- label
A boolean value where setting to

`TRUE`

ascribes node IDs to the label and`FALSE`

yields a blank label.- rel
An optional string for providing a relationship label to all edges to be added.

- node_aes
An optional list of named vectors comprising node aesthetic attributes. The helper function

`node_aes()`

is strongly recommended for use here as it contains arguments for each of the accepted node aesthetic attributes (e.g.,`shape`

,`style`

,`color`

,`fillcolor`

).- edge_aes
An optional list of named vectors comprising edge aesthetic attributes. The helper function

`edge_aes()`

is strongly recommended for use here as it contains arguments for each of the accepted edge aesthetic attributes (e.g.,`shape`

,`style`

,`penwidth`

,`color`

).- node_data
An optional list of named vectors comprising node data attributes. The helper function

`node_data()`

is strongly recommended for use here as it helps bind data specifically to the created nodes.- edge_data
An optional list of named vectors comprising edge data attributes. The helper function

`edge_data()`

is strongly recommended for use here as it helps bind data specifically to the created edges.- set_seed
Supplying a value sets a random seed of the

`Mersenne-Twister`

implementation.

## Examples

```
# Create an undirected GNP
# graph with 100 nodes using
# a probability value of 0.05
gnp_graph <-
create_graph(
directed = FALSE) %>%
add_gnp_graph(
n = 100,
p = 0.05)
# Get a count of nodes
gnp_graph %>% count_nodes()
#> [1] 100
# Get a count of edges
gnp_graph %>% count_edges()
#> [1] 212
```