R: Create an directed erdos renyi object

directed_erdos_renyi

R Documentation

Create an directed erdos renyi object

Description

Create an directed erdos renyi object

Usage

directed_erdos_renyi(
  n,
  ...,
  p = NULL,
  poisson_edges = TRUE,
  allow_self_loops = TRUE
)

Arguments

`n`	Number of nodes in graph.
`...`	Arguments passed on to `directed_factor_model` `expected_in_degree` If specified, the desired expected in degree of the graph. Specifying `expected_in_degree` simply rescales `S` to achieve this. Defaults to `NULL`. Specify only one of `expected_in_degree`, `expected_out_degree`, and `expected_density`. `expected_out_degree` If specified, the desired expected out degree of the graph. Specifying `expected_out_degree` simply rescales `S` to achieve this. Defaults to `NULL`. Specify only one of `expected_in_degree`, `expected_out_degree`, and `expected_density`.
`p`	Probability of an edge between any two nodes. You must specify either `p`, `expected_in_degree`, or `expected_out_degree`.
`poisson_edges`	Logical indicating whether or not multiple edges are allowed to form between a pair of nodes. Defaults to `TRUE`. When `FALSE`, sampling proceeds as usual, and duplicate edges are removed afterwards. Further, when `FALSE`, we assume that `S` specifies a desired between-factor connection probability, and back-transform this `S` to the appropriate Poisson intensity parameter to approximate Bernoulli factor connection probabilities. See Section 2.3 of Rohe et al. (2017) for some additional details.
`allow_self_loops`	Logical indicating whether or not nodes should be allowed to form edges with themselves. Defaults to `TRUE`. When `FALSE`, sampling proceeds allowing self-loops, and these are then removed after the fact.

Value

A directed_factor_model S3 class based on a list with the following elements:

X: The incoming latent positions as a Matrix() object.
S: The mixing matrix as a Matrix() object.
Y: The outgoing latent positions as a Matrix() object.
n: The number of nodes with incoming edges in the network.
k1: The dimension of the latent node position vectors encoding incoming latent communities (i.e. in X).
d: The number of nodes with outgoing edges in the network. Does not need to match n – rectangular adjacency matrices are supported.
k2: The dimension of the latent node position vectors encoding outgoing latent communities (i.e. in Y).
poisson_edges: Whether or not the graph is taken to be have Poisson or Bernoulli edges, as indicated by a logical vector of length 1.
allow_self_loops: Whether or not self loops are allowed.

Examples


set.seed(87)

er <- directed_erdos_renyi(n = 10, p = 0.1)
er

big_er <- directed_erdos_renyi(n = 10^6, expected_in_degree = 5)
big_er

A <- sample_sparse(er)
A

Create an directed erdos renyi object

Description

Usage

Arguments

Value

See Also

Examples