fastRG: Sample Generalized Random Dot Product Graphs in Linear Time
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.
| Version: | 0.4.0 |
| Depends: | Matrix |
| Imports: | dplyr,ggplot2,glue,igraph, methods,rlang (≥ 1.0.0),RSpectra, stats,tibble,tidygraph,tidyr |
| Suggests: | covr,irlba,knitr,magrittr,purrr,rmarkdown,scales,testthat (≥ 3.0.0) |
| Published: | 2025-12-06 |
| DOI: | 10.32614/CRAN.package.fastRG |
| Author: | Alex Hayes [aut, cre, cph], Karl Rohe [aut, cph], Jun Tao [aut], Xintian Han [aut], Norbert Binkiewicz [aut] |
| Maintainer: | Alex Hayes <alexpghayes at gmail.com> |
| BugReports: | https://github.com/RoheLab/fastRG/issues |
| License: | MIT + fileLICENSE |
| URL: | https://rohelab.github.io/fastRG/,https://github.com/RoheLab/fastRG |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | fastRG results |
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