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

add_gnm_graph {DiagrammeR}

R Documentation

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

Description

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.

Usage

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()

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

[Package DiagrammeR version 1.0.1 Index]