diff --git a/GNNGraphs/src/abstracttypes.jl b/GNNGraphs/src/abstracttypes.jl
index 1149157f2..73146160f 100644
--- a/GNNGraphs/src/abstracttypes.jl
+++ b/GNNGraphs/src/abstracttypes.jl
@@ -1,5 +1,5 @@
 
-const COO_T = Tuple{T, T, V} where {T <: AbstractVector{<:Integer}, V <: AbstractVector}
+const COO_T = Tuple{T, T, V} where {T <: AbstractVector{<:Integer}, V <: Union{Nothing, AbstractVector}}
 const ADJLIST_T = AbstractVector{T} where {T <: AbstractVector{<:Integer}}
 const ADJMAT_T = AbstractMatrix
 const SPARSE_T = AbstractSparseMatrix # subset of ADJMAT_T
diff --git a/GNNGraphs/src/generate.jl b/GNNGraphs/src/generate.jl
index 0b595ce33..872150103 100644
--- a/GNNGraphs/src/generate.jl
+++ b/GNNGraphs/src/generate.jl
@@ -1,5 +1,5 @@
 """
-    rand_graph(n, m; bidirected=true, seed=-1, edge_weight = nothing, kws...)
+    rand_graph([rng,] n, m; bidirected=true, edge_weight = nothing, kws...)
 
 Generate a random (Erdós-Renyi) `GNNGraph` with `n` nodes and `m` edges.
 
@@ -10,7 +10,7 @@ In any case, the output graph will contain no self-loops or multi-edges.
 A vector can be passed  as `edge_weight`. Its length has to be equal to `m`
 in the directed case, and `m÷2` in the bidirected one.
 
-Use a `seed > 0` for reproducibility.
+Pass a random number generator as the first argument to make the generation reproducible.
 
 Additional keyword arguments will be passed to the [`GNNGraph`](@ref) constructor.
 
diff --git a/GNNGraphs/src/transform.jl b/GNNGraphs/src/transform.jl
index 8e8c98d13..3710c545f 100644
--- a/GNNGraphs/src/transform.jl
+++ b/GNNGraphs/src/transform.jl
@@ -518,7 +518,6 @@ end
 Return a new graph obtained from `g` by adding random edges, based on a specified `perturb_ratio`. 
 The `perturb_ratio` determines the fraction of new edges to add relative to the current number of edges in the graph. 
 These new edges are added without creating self-loops. 
-Optionally, a random `seed` can be provided to ensure reproducible perturbations.
 
 The function returns a new `GNNGraph` instance that shares some of the underlying data with `g` but includes the additional edges. 
 The nodes for the new edges are selected randomly, and no edge data (`edata`) or weights (`w`) are assigned to these new edges.
diff --git a/GNNGraphs/test/generate.jl b/GNNGraphs/test/generate.jl
index 867fec399..263afb2e9 100644
--- a/GNNGraphs/test/generate.jl
+++ b/GNNGraphs/test/generate.jl
@@ -16,19 +16,23 @@
         @test g.edata.e[:, (m2 + 1):end] == e
     end
 
-    g = rand_graph(n, m, bidirected = false, seed = 17, graph_type = GRAPH_T)
+    rng = MersenneTwister(17)
+    g = rand_graph(rng, n, m, bidirected = false, graph_type = GRAPH_T)
     @test g.num_nodes == n
     @test g.num_edges == m
 
-    g2 = rand_graph(n, m, bidirected = false, seed = 17, graph_type = GRAPH_T)
+    rng = MersenneTwister(17)
+    g2 = rand_graph(rng, n, m, bidirected = false, graph_type = GRAPH_T)
     @test edge_index(g2) == edge_index(g)
 
     ew = rand(m2)
-    g = rand_graph(n, m, bidirected = true, seed = 17, graph_type = GRAPH_T, edge_weight = ew)
+    rng = MersenneTwister(17)
+    g = rand_graph(rng, n, m, bidirected = true, graph_type = GRAPH_T, edge_weight = ew)
     @test get_edge_weight(g) == [ew; ew] broken=(GRAPH_T != :coo)
     
     ew = rand(m)
-    g = rand_graph(n, m, bidirected = false, seed = 17, graph_type = GRAPH_T, edge_weight = ew)
+    rng = MersenneTwister(17)
+    g = rand_graph(n, m, bidirected = false, graph_type = GRAPH_T, edge_weight = ew)
     @test get_edge_weight(g) == ew broken=(GRAPH_T != :coo)
 end
 
diff --git a/GNNGraphs/test/utils.jl b/GNNGraphs/test/utils.jl
index ca0c25b17..0bc27d5ad 100644
--- a/GNNGraphs/test/utils.jl
+++ b/GNNGraphs/test/utils.jl
@@ -94,7 +94,8 @@
 end
 
 @testset "color_refinment" begin
-    g = rand_graph(10, 20, seed=17, graph_type = GRAPH_T)
+    rng = MersenneTwister(17)
+    g = rand_graph(rng, 10, 20, graph_type = GRAPH_T)
     x0 = ones(Int, 10)
     x, ncolors, niters = color_refinement(g, x0)
     @test ncolors == 8