View source: R/sample_edgelist.R
sample_edgelist | R Documentation |
There are two steps to using the fastRG
package. First,
you must parameterize a random dot product graph by
sampling the latent factors. Use functions such as
dcsbm()
, sbm()
, etc, to perform this specification.
Then, use sample_*()
functions to generate a random graph
in your preferred format.
sample_edgelist(factor_model, ...)
## S3 method for class 'undirected_factor_model'
sample_edgelist(factor_model, ...)
## S3 method for class 'directed_factor_model'
sample_edgelist(factor_model, ...)
factor_model |
A |
... |
Ignored. Do not use. |
This function implements the fastRG
algorithm as
described in Rohe et al (2017). Please see the paper
(which is short and open access!!) for details.
A single realization of a random Poisson (or Bernoulli)
Dot Product Graph, represented as a tibble::tibble()
with two
integer columns, from
and to
.
NOTE: Indices for isolated nodes will not appear in the edgelist! This can lead to issues if you construct network objects from the edgelist directly.
In the undirected case, from
and to
do not encode
information about edge direction, but we will always have
from <= to
for convenience of edge identification.
To avoid handling such considerations yourself, we recommend using
sample_sparse()
, sample_igraph()
, and sample_tidygraph()
over sample_edgelist()
.
Rohe, Karl, Jun Tao, Xintian Han, and Norbert Binkiewicz. 2017. "A Note on Quickly Sampling a Sparse Matrix with Low Rank Expectation." Journal of Machine Learning Research; 19(77):1-13, 2018. https://www.jmlr.org/papers/v19/17-128.html
Other samplers:
sample_edgelist.matrix()
,
sample_igraph()
,
sample_sparse()
,
sample_tidygraph()
library(igraph)
library(tidygraph)
set.seed(27)
##### undirected examples ----------------------------
n <- 100
k <- 5
X <- matrix(rpois(n = n * k, 1), nrow = n)
S <- matrix(runif(n = k * k, 0, .1), nrow = k)
# S will be symmetrized internal here, or left unchanged if
# it is already symmetric
ufm <- undirected_factor_model(
X, S,
expected_density = 0.1
)
ufm
### sampling graphs as edgelists ----------------------
edgelist <- sample_edgelist(ufm)
edgelist
### sampling graphs as sparse matrices ----------------
A <- sample_sparse(ufm)
inherits(A, "dsCMatrix")
isSymmetric(A)
dim(A)
B <- sample_sparse(ufm)
inherits(B, "dsCMatrix")
isSymmetric(B)
dim(B)
### sampling graphs as igraph graphs ------------------
sample_igraph(ufm)
### sampling graphs as tidygraph graphs ---------------
sample_tidygraph(ufm)
##### directed examples ----------------------------
n2 <- 100
k1 <- 5
k2 <- 3
d <- 50
X <- matrix(rpois(n = n2 * k1, 1), nrow = n2)
S <- matrix(runif(n = k1 * k2, 0, .1), nrow = k1, ncol = k2)
Y <- matrix(rexp(n = k2 * d, 1), nrow = d)
fm <- directed_factor_model(X, S, Y, expected_in_degree = 2)
fm
### sampling graphs as edgelists ----------------------
edgelist2 <- sample_edgelist(fm)
edgelist2
### sampling graphs as sparse matrices ----------------
A2 <- sample_sparse(fm)
inherits(A2, "dgCMatrix")
isSymmetric(A2)
dim(A2)
B2 <- sample_sparse(fm)
inherits(B2, "dgCMatrix")
isSymmetric(B2)
dim(B2)
### sampling graphs as igraph graphs ------------------
# since the number of rows and the number of columns
# in `fm` differ, we will get a bipartite igraph here
# creating the bipartite igraph is slow relative to other
# sampling -- if this is a blocker for
# you please open an issue and we can investigate speedups
dig <- sample_igraph(fm)
is_bipartite(dig)
### sampling graphs as tidygraph graphs ---------------
sample_tidygraph(fm)
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