This set of graph games creates graphs directly through sampling of different attributes, topologies, etc. The nature of their algorithm is described in detail at the linked igraph documentation.

play_degree(out_degree, in_degree = NULL, method = "simple")

play_dotprod(position, directed = TRUE)

play_fitness(m, out_fit, in_fit = NULL, loops = FALSE, multiple = FALSE)

play_fitness_power(
  n,
  m,
  out_exp,
  in_exp = -1,
  loops = FALSE,
  multiple = FALSE,
  correct = TRUE
)

play_gnm(n, m, directed = TRUE, loops = FALSE)

play_gnp(n, p, directed = TRUE, loops = FALSE)

play_geometry(n, radius, torus = FALSE)

play_erdos_renyi(n, p, m, directed = TRUE, loops = FALSE)

Arguments

out_degree, in_degree

The degrees of each node in the graph

method

The algorithm to use for the generation. Either 'simple', 'vl', or 'simple.no.multiple'

position

The latent position of each node by column.

directed

Should the resulting graph be directed

m

The number of edges in the graph

out_fit, in_fit

The fitness of each node

loops

Are loop edges allowed

multiple

Are multiple edges allowed

n

The number of nodes in the graph.

out_exp, in_exp

Power law exponent of degree distribution

correct

Use finite size correction

p

The probabilty of an edge occuring

radius

The radius within which vertices are connected

torus

Should the vertices be distributed on a torus instead of a plane

Value

A tbl_graph object

Functions

See also

Examples

plot(play_erdos_renyi(20, 0.3))
#> Warning: `play_erdos_renyi()` was deprecated in tidygraph 1.3.0.
#>  Please use `play_gnp()` instead.