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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