Sample Generalized Random Dot Product Graphs in Linear Time
Create an undirected Chung-Lu object
Create an undirected degree corrected stochastic blockmodel object
Create a directed degree corrected stochastic blockmodel object
Create an directed erdos renyi object
Create a directed factor model graph
Compute the eigendecomposition of the expected adjacency matrix of an ...
Create an undirected erdos renyi object
Calculate the expected adjacency matrix
Calculate the expected edges in Poisson RDPG graph
Create an undirected degree-corrected mixed membership stochastic bloc...
Create an undirected overlapping degree corrected stochastic blockmode...
Create an undirected planted partition object
Plot (expected) adjacency matrices
Objects exported from other packages
Low level interface to sample RPDG edgelists
Sample a random edgelist from a random dot product graph
Sample a random dot product graph as an igraph graph
Sample a random dot product graph as a sparse Matrix
Sample a random dot product graph as a tidygraph graph
Create an undirected stochastic blockmodel object
Compute the singular value decomposition of the expected adjacency mat...
Compute the singular value decomposition of the expected adjacency mat...
Create an undirected factor model graph
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.
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