R: Create an undirected Chung-Lu object

chung_lu {fastRG}

R Documentation

Create an undirected Chung-Lu object

Description

To specify a Chung-Lu graph, you must specify the degree-heterogeneity parameters (via n or theta). We provide reasonable defaults to enable rapid exploration or you can invest the effort for more control over the model parameters. We strongly recommend setting the expected_degree or expected_density argument to avoid large memory allocations associated with sampling large, dense graphs.

Usage

chung_lu(
  n = NULL,
  theta = NULL,
  ...,
  sort_nodes = TRUE,
  poisson_edges = TRUE,
  allow_self_loops = TRUE,
  force_identifiability = FALSE
)

Arguments

`n`	(degree heterogeneity) The number of nodes in the graph. Use when you don't want to specify the degree-heterogeneity parameters `theta` by hand. When `n` is specified, `theta` is randomly generated from a `LogNormal(2, 1)` distribution. This is subject to change, and may not be reproducible. `n` defaults to `NULL`. You must specify either `n` or `theta`, but not both.
`theta`	(degree heterogeneity) A numeric vector explicitly specifying the degree heterogeneity parameters. This implicitly determines the number of nodes in the resulting graph, i.e. it will have `length(theta)` nodes. Must be positive. Setting to a vector of ones recovers an erdos renyi graph. Defaults to `NULL`. You must specify either `n` or `theta`, but not both.
`...`	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. `expected_density` If specified, the desired expected density of the graph. Specifying `expected_density` simply rescales `S` to achieve this. Defaults to `NULL`. Do not specify both `expected_degree` and `expected_density` at the same time.
`sort_nodes`	Logical indicating whether or not to sort the nodes so that they are grouped by block and by `theta`. Useful for plotting. Defaults to `TRUE`.
`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.
`force_identifiability`	Logical indicating whether or not to normalize `theta` such that it sums to one within each block. Defaults to `FALSE`, since this behavior can be surprise when `theta` is set to a vector of all ones to recover the DC-SBM case.

Value

An undirected_chung_lu S3 object, a subclass of dcsbm().

Examples


set.seed(27)

cl <- chung_lu(n = 1000, expected_density = 0.01)
cl

theta <- round(stats::rlnorm(100, 2))

cl2 <- chung_lu(
  theta = theta,
  expected_degree = 5
)

cl2

edgelist <- sample_edgelist(cl)
edgelist