Samples generalized random product graphs, 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 using the fastRG algorithm of Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. 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.
Package details |
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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 <alexpghayes@gmail.com> |
License | MIT + file LICENSE |
Version | 0.3.2 |
URL | https://rohelab.github.io/fastRG/ https://github.com/RoheLab/fastRG |
Package repository | View on CRAN |
Installation |
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