Add a preferential attachment graph

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

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 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 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