diff --git a/docs/src/api/gnngraph.md b/docs/src/api/gnngraph.md
index 0a2e9ffdd..9f7cefde9 100644
--- a/docs/src/api/gnngraph.md
+++ b/docs/src/api/gnngraph.md
@@ -6,6 +6,10 @@ CurrentModule = GraphNeuralNetworks
 
 Documentation page for the graph type `GNNGraph` provided GraphNeuralNetworks.jl and its related methods. 
 
+Besides the methods documented here, one can rely on the large set of functionalities
+given by [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl)
+since `GNNGraph` inherits from `Graphs.AbstractGraph`.
+
 ## Index 
 
 ```@index
diff --git a/docs/src/gnngraph.md b/docs/src/gnngraph.md
index 4c9853e6a..bb0cabb6b 100644
--- a/docs/src/gnngraph.md
+++ b/docs/src/gnngraph.md
@@ -5,6 +5,9 @@ A GNNGraph `g` is a directed graph with nodes labeled from 1 to `g.num_nodes`.
 The underlying implementation allows for efficient application of graph neural network
 operators, gpu movement, and storage of node/edge/graph related feature arrays.
 
+`GNNGraph` inherits from [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl)'s `AbstractGraph`,
+therefore it supports most functionality from that library. 
+
 ## Graph Creation
 A GNNGraph can be created from several different data sources encoding the graph topology:
 
diff --git a/src/gnngraph.jl b/src/gnngraph.jl
index 2c8cccf58..e7e47ac30 100644
--- a/src/gnngraph.jl
+++ b/src/gnngraph.jl
@@ -11,28 +11,34 @@ const ADJMAT_T = AbstractMatrix
 const SPARSE_T = AbstractSparseMatrix # subset of ADJMAT_T
 const CUMAT_T = Union{AnyCuMatrix, CUDA.CUSPARSE.CuSparseMatrix}
 
-""" 
+"""
     GNNGraph(data; [graph_type, ndata, edata, gdata, num_nodes, graph_indicator, dir])
     GNNGraph(g::GNNGraph; [ndata, edata, gdata])
 
-A type representing a graph structure and storing also 
-feature arrays associated to nodes, edges, and to the whole graph (global features). 
+A type representing a graph structure that also stores 
+feature arrays associated to nodes, edges, and the graph itself. 
 
-A `GNNGraph` can be constructed out of different objects `data` expressing
-the connections inside the graph. The internal representation type
+A `GNNGraph` can be constructed out of different `data` objects 
+expressing the connections inside the graph. The internal representation type
 is determined by `graph_type`.
 
 When constructed from another `GNNGraph`, the internal graph representation
-is preserved and shared. The node/edge/global features are transmitted
-as well, unless explicitely changed though keyword arguments.
+is preserved and shared. The node/edge/graph features are retained
+as well, unless explicitely set by the keyword arguments
+`ndata`, `edata`, and `gdata`.
 
 A `GNNGraph` can also represent multiple graphs batched togheter 
 (see [`Flux.batch`](@ref) or [`SparseArrays.blockdiag`](@ref)).
 The field `g.graph_indicator` contains the graph membership
 of each node.
 
-A `GNNGraph` is a Graphs' `AbstractGraph`, therefore any functionality
-from the Graphs' graph library can be used on it.
+`GNNGraph`s are always directed graphs, therefore each edge is defined
+by a source node and a target node (see [`edge_index`](@ref)).
+Self loops (edges connecting a node to itself) and multiple edges
+(more than one edge between the same pair of nodes) are supported.
+
+A `GNNGraph` is a Graphs.jl's `AbstractGraph`, therefore it supports most 
+functionality from that library.
 
 # Arguments 
 
@@ -54,9 +60,9 @@ from the Graphs' graph library can be used on it.
         Possible values are `:out` and `:in`. Default `:out`.
 - `num_nodes`: The number of nodes. If not specified, inferred from `g`. Default `nothing`.
 - `graph_indicator`: For batched graphs, a vector containing the graph assigment of each node. Default `nothing`.  
-- `ndata`: Node features. A named tuple of arrays whose last dimension has size num_nodes.
-- `edata`: Edge features. A named tuple of arrays whose whose last dimension has size num_edges.
-- `gdata`: Global features. A named tuple of arrays whose has size num_graphs. 
+- `ndata`: Node features. A named tuple of arrays whose last dimension has size `num_nodes`.
+- `edata`: Edge features. A named tuple of arrays whose last dimension has size `num_edges`.
+- `gdata`: Graph features. A named tuple of arrays whose last dimension has size `num_graphs`. 
 
 # Usage. 
 
@@ -97,7 +103,7 @@ g = g |> gpu
 source, target = edge_index(g)
 ```
 """
-struct GNNGraph{T<:Union{COO_T,ADJMAT_T}}
+struct GNNGraph{T<:Union{COO_T,ADJMAT_T}} <: AbstractGraph{Int}
     graph::T
     num_nodes::Int
     num_edges::Int
diff --git a/src/utils.jl b/src/utils.jl
index 7f9506d27..b173b2898 100644
--- a/src/utils.jl
+++ b/src/utils.jl
@@ -39,8 +39,8 @@ function normalize_graphdata(data::NamedTuple; default_name, n, duplicate_if_nee
     
     if n == 1
         # If last array dimension is not 1, add a new dimension. 
-        # This is mostly usefule to reshape globale feature vectors
-        # of size D to Dx1 matrices.
+        # This is mostly useful to reshape graph feature vectors
+        # of size D into Dx1 matrices.
         function unsqz(v)
             if v isa AbstractArray && size(v)[end] != 1
                 v = reshape(v, size(v)..., 1)
diff --git a/test/gnngraph.jl b/test/gnngraph.jl
index 4eca56ea8..55383d183 100644
--- a/test/gnngraph.jl
+++ b/test/gnngraph.jl
@@ -277,6 +277,11 @@
         d = Flux.Data.DataLoader(g, batchsize = 2, shuffle=false)
         @test first(d) == getgraph(g, 1:2)
     end
+
+    @testset "Graphs.jl integration" begin
+        g = GNNGraph(erdos_renyi(10, 20))
+        @test g isa Graphs.AbstractGraph
+    end
 end