Add a G(n, m) Erdos-Renyi graph

To an existing graph object, add a graph built according to the Erdos-Renyi G(n, m) model. This uses the same constant probability when creating the fixed number of edges. Thus for n nodes there will be m edges and, if the loops argument is set as TRUE, then random loop edges will be part of m.

add_gnm_graph(graph, n, m, 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.
m	the number of edges in the generated graph.
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 GNM
# graph with 100 nodes and
# 120 edges
gnm_graph <-
  create_graph(
    directed = FALSE) %>%
  add_gnm_graph(
    n = 100,
    m = 120)

# Get a count of nodes
gnm_graph %>%
  count_nodes()
#> [1] 100

# Get a count of edges
gnm_graph %>%
  count_edges()
#> [1] 120