fastRG::sbm without setting expected_degree is doubling the number of edges that should be there. about fastrg HOT 3 CLOSED

library(fastRG)
#> Loading required package: Matrix
library(magrittr)

n <- 1000
pop <- n / 2
a <- .1
b <- .05
B <- matrix(c(a, b, b, a), nrow = 2)

b_model <- sbm(
  n = n, k = 2,
  B = B, edge_distribution = "bernoulli"
)

A <- sample_sparse(b_model)

mean(rowSums(A))  # double counts undirected edges
#> [1] 156.269
mean(rowSums(triu(A)))  # right way to count undirected edges in E[A]
#> [1] 78.194


pop * a + pop * b  # expected "undirected edge degree"
#> [1] 75

library(fastRG)
#> Loading required package: Matrix
library(magrittr)

n <- 1000
pop <- n / 2
a <- .1
b <- .05
B <- matrix(c(a, b, b, a), nrow = 2)

b_model <- sbm(
  n = n, k = 2,
  B = B, edge_distribution = "bernoulli"
)

A <- sample_sparse(b_model)

mean(rowSums(A))  # double counts undirected edges
#> [1] 156.845
mean(rowSums(triu(A)))  # right way to count undirected edges in E[A]
#> [1] 78.479


pop * a + pop * b  # expected "undirected edge degree"
#> [1] 75

model2 <- sbm(n = n, k = 2, B = B, edge_distribution = "bernoulli", expected_degree = 75)
A2 <- sample_sparse(model2)

mean(rowSums(A2))  # double counts undirected edges
#> [1] 76.393
mean(rowSums(triu(A2)))  # right way to count undirected edges in E[A]
#> [1] 38.225

library(fastRG)
#> Loading required package: Matrix
library(magrittr)

n <- 1000
pop <- n / 2
a <- .1
b <- .05
B <- matrix(c(a, b, b, a), nrow = 2)

b_model <- sbm(
  n = n, k = 2,
  B = B, edge_distribution = "bernoulli"
)

# model display corrected, both expected degree and density
b_model
#> Undirected Stochastic Blockmodel
#> --------------------------------
#> 
#> Nodes (n): 1000 (arranged by block)
#> Blocks (k): 2
#> 
#> Traditional SBM parameterization:
#> 
#> Block memberships (z): 1000 [factor] 
#> Block probabilities (pi): 2 [numeric] 
#> Edge distribution: bernoulli
#> 
#> Factor model parameterization:
#> 
#> X: 1000 x 2 [dgeMatrix] 
#> S: 2 x 2 [dsyMatrix] 
#> 
#> Expected edges: 78329
#> Expected degree: 78.3
#> Expected density: 0.15681

A <- sample_sparse(b_model)

mean(rowSums(A))  # double counts undirected edges
#> [1] 155.641
mean(rowSums(triu(A)))  # right way to count undirected edges in E[A]
#> [1] 77.876

sum(triu(A)) / choose(n, 2)  # correct "undirected" density
#> [1] 0.1559079


pop * a + pop * b  # expected "undirected edge degree"
#> [1] 75

model2 <- sbm(n = n, k = 2, B = B, edge_distribution = "bernoulli", expected_degree = 75)
A2 <- sample_sparse(model2)

mean(rowSums(A2))  # double counts undirected edges
#> [1] 155.995
mean(rowSums(triu(A2)))  # right way to count undirected edges in E[A]
#> [1] 78.053

sum(triu(A)) / choose(n, 2)  # correct "undirected" density 
#> [1] 0.1559079