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Sample Uniform or Regular Random Hypergraphs

Source: R/hypergraph_random.R
hg_sample_uniform.Rd

hg_sample_uniform() makes every hyperedge contain exactly k distinct nodes. hg_sample_regular() makes every node incident to exactly k distinct hyperedges. Sampling probabilities can be unequal.

Usage

hg_sample_uniform(n, m, k, prob = NULL, seed = NULL)

hg_sample_regular(n, m, k, prob = NULL, seed = NULL)

Arguments

n

Number of nodes.

m

Number of hyperedges. For hg_sample_gnp(), NULL draws it from a Poisson distribution. For the uniform and regular models it is required.

k

Hyperedge size for the uniform model; node degree for the regular model.

prob

Sampling weights: length n for the uniform model and length m for the regular model. NULL is uniform.

seed

Optional reproducibility seed. The caller's RNG state is restored on exit.

Value

A net_hg.

References

Marchette, D. J. (2021). HyperG: Hypergraphs in R. R package version 1.0.0.

Examples

u <- hg_sample_uniform(20, 8, k = 3, seed = 1)
r <- hg_sample_regular(20, 8, k = 2, seed = 1)
hg_measures(u, what = "distribution", measure = "size")
#>   value n proportion ccdf
#> 1     3 8          1    1
hg_measures(r, what = "distribution", measure = "hyperdegree")
#>   value  n proportion ccdf
#> 1     2 20          1    1