Add a G(n, p) Erdos-Renyi graph — add_gnp

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.

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.

Value

A graph object of class dgr_graph.

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