xgi.generators.uniform

Generate random uniform hypergraphs.

Functions

xgi.generators.uniform.uniform_hypergraph_configuration_model(k, m, seed=None)[source]

A function to generate an m-uniform configuration model

Parameters:

k (dictionary) – This is a dictionary where the keys are node ids and the values are node degrees.
m (int) – specifies the hyperedge size
seed (integer or None (default)) – The seed for the random number generator

Returns:

The generated hypergraph

Return type:

Hypergraph object

Warns:

warnings.warn – If the sums of the degrees are not divisible by m, the algorithm still runs, but raises a warning and adds an additional connection to random nodes to satisfy this condition.

Notes

This algorithm normally creates multi-edges and loopy hyperedges. We remove the loopy hyperedges.

References

“The effect of heterogeneity on hypergraph contagion models” by Nicholas W. Landry and Juan G. Restrepo https://doi.org/10.1063/5.0020034

Example

>>> import xgi
>>> import random
>>> n = 1000
>>> m = 3
>>> k = {1: 1, 2: 2, 3: 3, 4: 3}
>>> H = xgi.uniform_hypergraph_configuration_model(k, m)

xgi.generators.uniform.uniform_erdos_renyi_hypergraph(n, m, p, p_type='degree', seed=None)[source]

Generate an m-uniform Erdős–Rényi hypergraph

This creates a hypergraph with n nodes where hyperedges of size m are created at random to obtain a mean degree of k.

Parameters:

n (int > 0) – Number of nodes
m (int > 0) – Hyperedge size
p (float or int > 0) – Mean expected degree if p_type=”degree” and probability of an m-hyperedge if p_type=”prob”
p_type (str) – “degree” or “prob”, by default “degree”
seed (integer or None (default)) – The seed for the random number generator

Returns:

The Erdos Renyi hypergraph

Return type:

Hypergraph