fastRG: Sample Generalized Random Dot Product Graphs in Linear Time (original) (raw)
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: | 0.3.2 |
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Depends: | Matrix |
Imports: | dplyr, ellipsis, ggplot2, glue, igraph, methods, RSpectra, stats, tibble, tidygraph, tidyr |
Suggests: | covr, knitr, magrittr, rmarkdown, testthat (≥ 3.0.0) |
Published: | 2023-08-21 |
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 |
BugReports: | https://github.com/RoheLab/fastRG/issues |
License: | MIT + file |
URL: | https://rohelab.github.io/fastRG/,https://github.com/RoheLab/fastRG |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | fastRG results |
Documentation:
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Reverse dependencies:
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