diff --git a/GNNGraphs/src/convert.jl b/GNNGraphs/src/convert.jl
index 6735a9d27..de129ab92 100644
--- a/GNNGraphs/src/convert.jl
+++ b/GNNGraphs/src/convert.jl
@@ -137,7 +137,7 @@ function to_dense(A::ADJMAT_T, T = nothing; dir = :out, num_nodes = nothing,
         A = T.(A)
     end
     if !weighted
-        A = map(x -> ifelse(x > 0, T(1), T(0)), A)
+        A = binarize(A, T)
     end
     return A, num_nodes, num_edges
 end
diff --git a/GNNGraphs/test/query.jl b/GNNGraphs/test/query.jl
index 54aab409b..f5c08001e 100644
--- a/GNNGraphs/test/query.jl
+++ b/GNNGraphs/test/query.jl
@@ -272,6 +272,15 @@ end
 
             @test gw == [1, 1, 1]
         end
+        
+        @testset  "fmt" begin
+            Asparse = adjacency_matrix(g, weighted = false, fmt = :sparse)
+            @test Asparse isa AbstractSparseMatrix 
+            Adense = adjacency_matrix(g, weighted = false, fmt = :dense)
+            @test Adense isa AbstractMatrix
+            @test !(Adense isa AbstractSparseMatrix)
+            @test collect(Asparse) == Adense
+        end
     end
 
     @testset "khop_adj" begin
diff --git a/GNNlib/ext/GNNlibCUDAExt.jl b/GNNlib/ext/GNNlibCUDAExt.jl
index 9cfe45089..8a231c768 100644
--- a/GNNlib/ext/GNNlibCUDAExt.jl
+++ b/GNNlib/ext/GNNlibCUDAExt.jl
@@ -3,7 +3,7 @@ module GNNlibCUDAExt
 using CUDA
 using Random, Statistics, LinearAlgebra
 using GNNlib: GNNlib, propagate, copy_xj, e_mul_xj, w_mul_xj
-using GNNGraphs: GNNGraph, COO_T, SPARSE_T, to_dense, to_sparse
+using GNNGraphs: GNNGraph, COO_T, SPARSE_T, to_dense, to_sparse, adjacency_matrix
 using ChainRulesCore: @non_differentiable
 
 const CUDA_COO_T = Tuple{T, T, V} where {T <: AnyCuArray{<:Integer}, V <: Union{Nothing, AnyCuArray}}