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. 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: 0.3.0
Depends: Matrix
Imports: ellipsis, glue, igraph, magrittr, RSpectra, stats, tibble, tidygraph
Suggests: covr, dplyr, ggplot2, knitr, rmarkdown, testthat (≥ 2.1.0)
Published: 2021-02-26
Author: Alex Hayes ORCID iD [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://github.com/RoheLab/fastRG
NeedsCompilation: no
Materials: README NEWS
CRAN checks: fastRG results

Downloads:

Reference manual: fastRG.pdf
Package source: fastRG_0.3.0.tar.gz
Windows binaries: r-devel: fastRG_0.3.0.zip, r-release: fastRG_0.3.0.zip, r-oldrel: fastRG_0.3.0.zip
macOS binaries: r-release (arm64): fastRG_0.3.0.tgz, r-release (x86_64): fastRG_0.3.0.tgz, r-oldrel: fastRG_0.3.0.tgz

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