diff --git a/.github/workflows/tests_GNNGraphs.yml b/.github/workflows/tests_GNNGraphs.yml
index 3d28ee93c..43e642e1c 100644
--- a/.github/workflows/tests_GNNGraphs.yml
+++ b/.github/workflows/tests_GNNGraphs.yml
@@ -15,7 +15,7 @@ jobs:
       matrix:
         version:
           - '1.10' # Replace this with the minimum Julia version that your package supports.
-          - '1' #  '1' will automatically expand to the latest stable 1.x release of Julia.
+          # - '1' #  '1' will automatically expand to the latest stable 1.x release of Julia.
           # - 'pre'
         os:
           - ubuntu-latest
diff --git a/.github/workflows/tests_GraphNeuralNetworks.yml b/.github/workflows/tests_GraphNeuralNetworks.yml
index 0140dab69..92427515f 100644
--- a/.github/workflows/tests_GraphNeuralNetworks.yml
+++ b/.github/workflows/tests_GraphNeuralNetworks.yml
@@ -15,7 +15,7 @@ jobs:
       matrix:
         version:
           - '1.10' # Replace this with the minimum Julia version that your package supports.
-          - '1' #  '1' will automatically expand to the latest stable 1.x release of Julia.
+          # - '1' #  '1' will automatically expand to the latest stable 1.x release of Julia.
           # - 'pre'
         os:
           - ubuntu-latest
diff --git a/GNNGraphs/Project.toml b/GNNGraphs/Project.toml
index d8603760d..ff30d3f6e 100644
--- a/GNNGraphs/Project.toml
+++ b/GNNGraphs/Project.toml
@@ -1,7 +1,7 @@
 name = "GNNGraphs"
 uuid = "aed8fd31-079b-4b5a-b342-a13352159b8c"
 authors = ["Carlo Lucibello and contributors"]
-version = "0.1.0"
+version = "1.0.0"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
@@ -46,7 +46,7 @@ SparseArrays = "1"
 Statistics = "1"
 StatsBase = "0.34"
 cuDNN = "1"
-julia = "1.9"
+julia = "1.10"
 
 [extras]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
diff --git a/GNNGraphs/ext/GNNGraphsCUDAExt.jl b/GNNGraphs/ext/GNNGraphsCUDAExt.jl
new file mode 100644
index 000000000..0d839f58c
--- /dev/null
+++ b/GNNGraphs/ext/GNNGraphsCUDAExt.jl
@@ -0,0 +1,33 @@
+module GNNGraphsCUDAExt
+
+using CUDA
+using Random, Statistics, LinearAlgebra
+using GNNGraphs
+using GNNGraphs: COO_T, ADJMAT_T, SPARSE_T 
+
+const CUMAT_T = Union{CUDA.AnyCuMatrix, CUDA.CUSPARSE.CuSparseMatrix}
+
+# Query 
+
+GNNGraphs._rand_dense_vector(A::CUMAT_T) = CUDA.randn(size(A, 1))
+
+# Transform
+
+GNNGraphs.dense_zeros_like(a::CUMAT_T, T::Type, sz = size(a)) = CUDA.zeros(T, sz)
+
+
+# Utils
+
+GNNGraphs.iscuarray(x::AnyCuArray) = true
+
+
+function sort_edge_index(u::AnyCuArray, v::AnyCuArray)
+    dev = get_device(u)
+    cdev = cpu_device()
+    u, v = u |> cdev, v |> cdev
+    #TODO proper cuda friendly implementation
+    sort_edge_index(u, v) |> dev
+end
+
+
+end #module
diff --git a/GNNGraphs/ext/GNNGraphsCUDAExt/GNNGraphsCUDAExt.jl b/GNNGraphs/ext/GNNGraphsCUDAExt/GNNGraphsCUDAExt.jl
deleted file mode 100644
index f7a053b97..000000000
--- a/GNNGraphs/ext/GNNGraphsCUDAExt/GNNGraphsCUDAExt.jl
+++ /dev/null
@@ -1,14 +0,0 @@
-module GNNGraphsCUDAExt
-
-using CUDA
-using Random, Statistics, LinearAlgebra
-using GNNGraphs
-using GNNGraphs: COO_T, ADJMAT_T, SPARSE_T 
-
-const CUMAT_T = Union{CUDA.AnyCuMatrix, CUDA.CUSPARSE.CuSparseMatrix}
-
-include("query.jl")
-include("transform.jl")
-include("utils.jl")
-
-end #module
diff --git a/GNNGraphs/ext/GNNGraphsCUDAExt/query.jl b/GNNGraphs/ext/GNNGraphsCUDAExt/query.jl
deleted file mode 100644
index 0e74f725e..000000000
--- a/GNNGraphs/ext/GNNGraphsCUDAExt/query.jl
+++ /dev/null
@@ -1,2 +0,0 @@
-
-GNNGraphs._rand_dense_vector(A::CUMAT_T) = CUDA.randn(size(A, 1))
diff --git a/GNNGraphs/ext/GNNGraphsCUDAExt/transform.jl b/GNNGraphs/ext/GNNGraphsCUDAExt/transform.jl
deleted file mode 100644
index d2ee417fc..000000000
--- a/GNNGraphs/ext/GNNGraphsCUDAExt/transform.jl
+++ /dev/null
@@ -1,2 +0,0 @@
-
-GNNGraphs.dense_zeros_like(a::CUMAT_T, T::Type, sz = size(a)) = CUDA.zeros(T, sz)
diff --git a/GNNGraphs/ext/GNNGraphsCUDAExt/utils.jl b/GNNGraphs/ext/GNNGraphsCUDAExt/utils.jl
deleted file mode 100644
index 0083d1db5..000000000
--- a/GNNGraphs/ext/GNNGraphsCUDAExt/utils.jl
+++ /dev/null
@@ -1,11 +0,0 @@
-
-GNNGraphs.iscuarray(x::AnyCuArray) = true
-
-
-function sort_edge_index(u::AnyCuArray, v::AnyCuArray)
-    dev = get_device(u)
-    cdev = cpu_device()
-    u, v = u |> cdev, v |> cdev
-    #TODO proper cuda friendly implementation
-    sort_edge_index(u, v) |> dev
-end
diff --git a/GNNGraphs/src/GNNGraphs.jl b/GNNGraphs/src/GNNGraphs.jl
index 80a764a78..c0dbf7678 100644
--- a/GNNGraphs/src/GNNGraphs.jl
+++ b/GNNGraphs/src/GNNGraphs.jl
@@ -80,7 +80,6 @@ export add_nodes,
        perturb_edges,
        remove_nodes,
        ppr_diffusion,
-       drop_nodes,
 # from MLUtils
        batch,
        unbatch,
diff --git a/GNNGraphs/src/transform.jl b/GNNGraphs/src/transform.jl
index 9520d63ad..8df726752 100644
--- a/GNNGraphs/src/transform.jl
+++ b/GNNGraphs/src/transform.jl
@@ -307,35 +307,27 @@ function remove_nodes(g::GNNGraph{<:COO_T}, nodes_to_remove::AbstractVector)
 end
 
 """
-    drop_nodes(g::GNNGraph{<:COO_T}, p)
+    remove_nodes(g::GNNGraph, p)
 
-Randomly drop nodes (and their associated edges) from a GNNGraph based on a given probability. 
-Dropping nodes is a technique that can be used for graph data augmentation, refering paper [DropNode](https://arxiv.org/pdf/2008.12578.pdf).
+Returns a new graph obtained by dropping nodes from `g` with independent probabilities `p`. 
 
-# Arguments
-- `g`: The input graph from which nodes (and their associated edges) will be dropped.
-- `p`: The probability of dropping each node. Default value is `0.5`.
-
-# Returns
-A modified GNNGraph with nodes (and their associated edges) dropped based on the given probability.
+# Examples
 
-# Example
 ```julia
-using GraphNeuralNetworks
-# Construct a GNNGraph
-g = GNNGraph([1, 1, 2, 2, 3], [2, 3, 1, 3, 1], num_nodes=3)
-# Drop nodes with a probability of 0.5
-g_new = drop_node(g, 0.5)
-println(g_new)
+julia> g = GNNGraph([1, 1, 2, 2, 3, 4], [1, 2, 3, 1, 3, 1])
+GNNGraph:
+  num_nodes: 4
+  num_edges: 6
+
+julia> g_new = remove_nodes(g, 0.5)
+GNNGraph:
+  num_nodes: 2
+  num_edges: 2
 ```
 """
-function drop_nodes(g::GNNGraph{<:COO_T}, p = 0.5)
-    num_nodes = g.num_nodes
-    nodes_to_remove = filter(_ -> rand() < p, 1:num_nodes)
-    
-    new_g = remove_nodes(g, nodes_to_remove)
-    
-    return new_g
+function remove_nodes(g::GNNGraph, p::AbstractFloat)
+    nodes_to_remove = filter(_ -> rand() < p, 1:g.num_nodes)
+    return remove_nodes(g, nodes_to_remove)
 end
 
 """
diff --git a/GNNGraphs/test/transform.jl b/GNNGraphs/test/transform.jl
index 23d92a7de..993ac714a 100644
--- a/GNNGraphs/test/transform.jl
+++ b/GNNGraphs/test/transform.jl
@@ -247,20 +247,20 @@ end end
     @test edata_new == edatatest
 end end
 
-@testset "drop_nodes" begin
+@testset "remove_nodes(g, p)" begin
     if GRAPH_T == :coo
         Random.seed!(42)
         s = [1, 1, 2, 3]
         t = [2, 3, 4, 5]
         g = GNNGraph(s, t, graph_type = GRAPH_T)    
         
-        gnew = drop_nodes(g, Float32(0.5))
+        gnew = remove_nodes(g, 0.5)
         @test gnew.num_nodes == 3
 
-        gnew = drop_nodes(g, Float32(1.0))
+        gnew = remove_nodes(g, 1.0)
         @test gnew.num_nodes == 0
 
-        gnew = drop_nodes(g, Float32(0.0))
+        gnew = remove_nodes(g, 0.0)
         @test gnew.num_nodes == 5
     end
 end