fastRG: Sample Generalized Random Dot Product Graphs in Linear Time
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 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: |
0.3.1 |
| Depends: |
Matrix |
| Imports: |
ellipsis, glue, igraph, RSpectra, stats, tibble, tidygraph |
| Suggests: |
covr, dplyr, ggplot2, knitr, magrittr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2022-06-30 |
| 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 + file LICENSE |
| 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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