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Create an undirected planted partition object

Source: R/undirected_planted_partition.R
planted_partition.Rd

To specify a planted partition model, you must specify the number of nodes (via n), the mixing matrix (optional, either via within_block/between_block or a/b), and the relative block probabilites (optional, via pi). We provide defaults for most of these options 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

planted_partition(
  n,
  k,
  ...,
  within_block = NULL,
  between_block = NULL,
  a = NULL,
  b = NULL,
  pi = rep(1/k, k),
  sort_nodes = TRUE,
  poisson_edges = TRUE,
  allow_self_loops = TRUE
)

Arguments

n

The number of nodes in the network. Must be a positive integer. This argument is required.

k

Number of planted partitions, as a positive integer. This argument is required.

...

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.

within_block

Probability of within block edges. Must be strictly between zero and one. Must specify either within_block and between_block, or a and b to determine edge probabilities.

between_block

Probability of between block edges. Must be strictly between zero and one. Must specify either within_block and between_block, or a and b to determine edge probabilities.

a

Integer such that a/n is the probability of edges within a block. Useful for sparse graphs. Must specify either within_block and between_block, or a and b to determine edge probabilities.

b

Integer such that b/n is the probability of edges between blocks. Useful for sparse graphs. Must specify either within_block and between_block, or a and b to determine edge probabilities.

pi

(relative block probabilities) Relative block probabilities. Must be positive, but do not need to sum to one, as they will be normalized internally. Must match the dimensions of B or k. Defaults to rep(1 / k, k), or a balanced blocks.

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.

Value

An undirected_planted_partition S3 object, which is a subclass of the sbm() object, with additional fields:

  • within_block: The probability of edge formation within a block.

  • between_block: The probability of edge formation between two distinct blocks.

Details

A planted partition model is stochastic blockmodel in which the diagonal and the off-diagonal of the mixing matrix B are both constant. This means that edge probabilities depend only on whether two nodes belong to the same block, or to different blocks, but the particular blocks themselves don't have any impact apart from this.

See also

Other stochastic block models: dcsbm(), directed_dcsbm(), mmsbm(), overlapping_sbm(), sbm()

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

Examples


set.seed(27)

lazy_pp <- planted_partition(
  n = 1000,
  k = 5,
  expected_density = 0.01,
  within_block = 0.1,
  between_block = 0.01
)

lazy_pp
#> Undirected Stochastic Blockmodel
#> --------------------------------
#> 
#> Nodes (n): 1000 (arranged by block)
#> Blocks (k): 5
#> 
#> Traditional SBM parameterization:
#> 
#> Block memberships (z): 1000 [factor] 
#> Block probabilities (pi): 5 [numeric] 
#> Factor model parameterization:
#> 
#> X: 1000 x 5 [dgCMatrix] 
#> S: 5 x 5 [dsyMatrix] 
#> 
#> Poisson edges: TRUE 
#> Allow self loops: TRUE 
#> 
#> Expected edges: 4995
#> Expected degree: 5
#> Expected density: 0.01