Samples generalized random product graph, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries. The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.
|Author||Alex Hayes [aut, cre, cph] (<https://orcid.org/0000-0002-4985-5160>), Karl Rohe [aut, cph], Jun Tao [aut], Xintian Han [aut], Norbert Binkiewicz [aut]|
|Maintainer||Alex Hayes <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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