DiagrammeR: add_pa_graph – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

add_pa_graph

Add a preferential attachment graph

Description

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()

# Get a count of edges
pa_graph %>% count_edges()

DiagrammeR

Graph/Network Visualization

v1.0.6.1

MIT + file LICENSE

Authors

Richard Iannone [aut, cre] (<https://orcid.org/0000-0003-3925-190X>)

Initial release