Add a preferential attachment graph — add_pa

To an existing graph object, add a graph built according to the Barabasi-Albert model, which uses preferential attachment in its stochastic algorithm.

Usage

add_pa_graph(
  graph,
  n,
  m = NULL,
  power = 1,
  out_dist = NULL,
  use_total_degree = FALSE,
  zero_appeal = 1,
  algo = "psumtree",
  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 preferential attachment graph.
m: The number of edges to add in each time step.
power: The power of the preferential attachment. The default value of 1 indicates a linear preferential attachment.
out_dist: A numeric vector that provides the distribution of the number of edges to add in each time step.
use_total_degree: A logical value (default is TRUE) that governs whether the total degree should be used for calculating the citation probability. If FALSE, the indegree is used.
zero_appeal: A measure of the attractiveness of the nodes with no adjacent edges.
algo: The algorithm to use to generate the graph. The available options are psumtree, psumtree-multiple, and bag. With the psumtree algorithm, a partial prefix-sum tree is used to to create the graph. Any values for power and zero_appeal can be provided and this algorithm never generates multiple edges. The psumtree-multiple algorithm also uses a partial prefix-sum tree but the difference here is that multiple edges are allowed. The bag algorithm places the node IDs into a bag as many times as their in-degree (plus once more). The required number of cited nodes are drawn from the bag with replacement. Multiple edges may be produced using this method (it is not disallowed).
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 an undirected PA
# graph with 100 nodes, adding
# 2 edges at every time step
pa_graph <-
  create_graph(
    directed = FALSE) %>%
  add_pa_graph(
    n = 100,
    m = 1)

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

# Get a count of edges
pa_graph %>% count_edges()
#> [1] 99