erdos_renyi {fastRG}

Create an undirected erdos renyi object

Description

Create an undirected erdos renyi object

Usage

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

Arguments

n

Number of nodes in graph.

...

Arguments passed on to undirected_factor_model

expected_degree

If specified, the desired expected degree of the graph. Specifying expected_degree simply rescales S to achieve this. Defaults to NULL. Do not specify both expected_degree and expected_density at the same time.

p

Probability of an edge between any two nodes. You must specify either p or expected_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

An undirected_factor_model S3 class based on a list with the following elements:

• X: The latent positions as a Matrix() object.

• S: The mixing matrix as a Matrix() object.

• n: The number of nodes in the network.

• k: The rank of expectation matrix. Equivalently, the dimension of the latent node position vectors.

See Also

Other erdos renyi: directed_erdos_renyi()

Other undirected graphs: chung_lu(), dcsbm(), mmsbm(), overlapping_sbm(), planted_partition(), sbm()

Examples

set.seed(87)

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


er <- erdos_renyi(n = 10, expected_density = 0.1)
er

big_er <- erdos_renyi(n = 10^6, expected_degree = 5)
big_er

A <- sample_sparse(er)
A