# Create a random growing graph with m edges added per step

Source:`R/add_growing_graph.R`

`add_growing_graph.Rd`

To an existing graph object, add a graph built by adding `m`

new edges at
each time step (where a node is added).

## Usage

```
add_growing_graph(
graph,
n,
m = 1,
citation = 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.

- m
The number of edges added per time step.

- citation
A logical value (default is

`FALSE`

) that governs whether a citation graph is to be created. This is where new edges specifically originate from the newly added node in the most recent time step.- type
An optional string that describes the entity type for all the nodes to be added.

- label
A logical 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 a random, growing
# citation graph with 100
# nodes, adding an edge after
# each node addition
growing_graph <-
create_graph() %>%
add_growing_graph(
n = 100,
m = 1,
citation = TRUE,
set_seed = 23)
# Get a count of nodes
growing_graph %>% count_nodes()
#> [1] 100
# Get a count of edges
growing_graph %>% count_edges()
#> [1] 99
```