Package: fastRG 0.3.2.9000

Alex Hayes

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.

Authors:Alex Hayes [aut, cre, cph], Karl Rohe [aut, cph], Jun Tao [aut], Xintian Han [aut], Norbert Binkiewicz [aut]

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fastRG/json (API)
NEWS

# Install 'fastRG' in R:
install.packages('fastRG', repos = c('https://rohelab.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/rohelab/fastrg/issues

On CRAN:

adjacency-matrixgraph-samplinglatent-factors

27 exports 5 stars 1.47 score 41 dependencies 22 scripts 264 downloads

Last updated 21 days agofrom:34886a102e. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 27 2024
R-4.5-winNOTEAug 27 2024
R-4.5-linuxNOTEAug 27 2024
R-4.4-winOKAug 27 2024
R-4.4-macOKAug 27 2024
R-4.3-winOKAug 27 2024
R-4.3-macOKAug 27 2024

Exports:chung_ludcsbmdirected_dcsbmdirected_erdos_renyidirected_factor_modeleigs_symerdos_renyiexpectationexpected_degreeexpected_degreesexpected_densityexpected_edgesexpected_in_degreeexpected_out_degreemmsbmoverlapping_sbmplanted_partitionplot_dense_matrixplot_expectationplot_sparse_matrixsample_edgelistsample_igraphsample_sparsesample_tidygraphsbmsvdsundirected_factor_model

Dependencies:clicolorspacecpp11dplyrfansifarvergenericsggplot2gluegtableigraphisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpurrrR6RColorBrewerRcppRcppEigenrlangRSpectrascalesstringistringrtibbletidygraphtidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Create an undirected Chung-Lu objectchung_lu
Create an undirected degree corrected stochastic blockmodel objectdcsbm
Create a directed degree corrected stochastic blockmodel objectdirected_dcsbm
Create an directed erdos renyi objectdirected_erdos_renyi
Create a directed factor model graphdirected_factor_model
Compute the eigendecomposition of the expected adjacency matrix of an undirected factor modeleigs_sym.undirected_factor_model
Create an undirected erdos renyi objecterdos_renyi
Calculate the expected adjacency matrixexpectation expectation.directed_factor_model expectation.undirected_factor_model
Calculate the expected edges in Poisson RDPG graphexpected_degree expected_degrees expected_density expected_edges expected_in_degree expected_out_degree
Create an undirected degree-corrected mixed membership stochastic blockmodel objectmmsbm
Create an undirected overlapping degree corrected stochastic blockmodel objectoverlapping_sbm
Create an undirected planted partition objectplanted_partition
Plot (expected) adjacency matricesplot_dense_matrix plot_expectation plot_sparse_matrix
Sample a random edgelist from a random dot product graphsample_edgelist sample_edgelist.directed_factor_model sample_edgelist.undirected_factor_model
Low level interface to sample RPDG edgelistssample_edgelist.Matrix sample_edgelist.matrix
Sample a random dot product graph as an igraph graphsample_igraph sample_igraph.directed_factor_model sample_igraph.undirected_factor_model
Sample a random dot product graph as a sparse Matrixsample_sparse sample_sparse.directed_factor_model sample_sparse.undirected_factor_model
Sample a random dot product graph as a tidygraph graphsample_tidygraph sample_tidygraph.directed_factor_model sample_tidygraph.undirected_factor_model
Create an undirected stochastic blockmodel objectsbm
Compute the singular value decomposition of the expected adjacency matrix of a directed factor modelsvds.directed_factor_model
Compute the singular value decomposition of the expected adjacency matrix of an undirected factor modelsvds.undirected_factor_model
Create an undirected factor model graphundirected_factor_model