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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.4.0 |
rolling source/ R- | fastRG_0.4.0.tar.gz |
316.2 KiB |
0.4.0 |
latest source/ R- | fastRG_0.4.0.tar.gz |
316.2 KiB |
0.4.0 |
2026-04-23 source/ R- | fastRG_0.4.0.tar.gz |
316.2 KiB |
0.4.0 |
2026-04-09 windows/windows R-4.5 | fastRG_0.4.0.zip |
498.2 KiB |