diff --git a/GNNGraphs/src/gnnheterograph/query.jl b/GNNGraphs/src/gnnheterograph/query.jl
index 831e654eb..6fdad7735 100644
--- a/GNNGraphs/src/gnnheterograph/query.jl
+++ b/GNNGraphs/src/gnnheterograph/query.jl
@@ -89,3 +89,31 @@ function graph_indicator(g::GNNHeteroGraph, node_t::Symbol)
     end
     return gi
 end
+
+"""
+    edge_features(g::GNNHeteroGraph)
+
+Return the edge features for a heterogeneous graph with a single edge type.
+If the graph has multiple edge types, this will error.
+If no edge features are present, returns `nothing`.
+"""
+function edge_features(g::GNNHeteroGraph)
+    if isempty(g.edata)
+        return nothing
+    elseif length(g.edata) > 1
+        @error "Multiple edge types present, access edge features directly through `g.edata[edge_t]`"
+    else
+        edata = only(values(g.edata))
+        if edata isa AbstractArray
+            return isempty(edata) ? nothing : edata
+        else 
+            if isempty(edata)
+                return nothing
+            elseif length(edata) > 1
+                @error "Multiple edge feature arrays present, access directly through `g.edata[edge_t]`"
+            else
+                return first(values(edata))
+            end
+        end
+    end
+end
\ No newline at end of file
diff --git a/GNNlib/src/layers/conv.jl b/GNNlib/src/layers/conv.jl
index 0827b4592..fdbefc3f3 100644
--- a/GNNlib/src/layers/conv.jl
+++ b/GNNlib/src/layers/conv.jl
@@ -257,11 +257,12 @@ end
 
 ####################### NNConv ######################################
 
-function nn_conv(l, g::GNNGraph, x::AbstractMatrix, e)
+function nn_conv(l, g::AbstractGNNGraph, x, e)
     check_num_nodes(g, x)
+    xj, xi = expand_srcdst(g, x)
     message = Fix1(nn_conv_message, l)
-    m = propagate(message, g, l.aggr, xj = x, e = e)
-    return l.σ.(l.weight * x .+ m .+ l.bias)
+    m = propagate(message, g, l.aggr, xj = xj, e = e)
+    return l.σ.(l.weight * xi .+ m .+ l.bias)
 end
 
 function nn_conv_message(l, xi, xj, e)
diff --git a/GraphNeuralNetworks/src/layers/conv.jl b/GraphNeuralNetworks/src/layers/conv.jl
index e3cf30fea..a085ba848 100644
--- a/GraphNeuralNetworks/src/layers/conv.jl
+++ b/GraphNeuralNetworks/src/layers/conv.jl
@@ -718,6 +718,8 @@ end
 
 (l::NNConv)(g, x, e) = GNNlib.nn_conv(l, g, x, e)
 
+(l::NNConv)(g, x) = GNNlib.nn_conv(l, g, x, edge_features(g))
+
 (l::NNConv)(g::GNNGraph) = GNNGraph(g, ndata = l(g, node_features(g), edge_features(g)))
 
 function Base.show(io::IO, l::NNConv)
diff --git a/GraphNeuralNetworks/test/layers/heteroconv.jl b/GraphNeuralNetworks/test/layers/heteroconv.jl
index 4a6d40ca3..5a888698e 100644
--- a/GraphNeuralNetworks/test/layers/heteroconv.jl
+++ b/GraphNeuralNetworks/test/layers/heteroconv.jl
@@ -155,7 +155,22 @@
         x = (A = rand(Float32, 4,2), B = rand(Float32, 4, 3))
         layers = HeteroGraphConv( (:A, :to, :B) => GCNConv(4 => 2, tanh),
                                     (:B, :to, :A) => GCNConv(4 => 2, tanh));
-        y = layers(g, x); 
+        y = layers(g, x);
         @test size(y.A) == (2,2) && size(y.B) == (2,3)
     end
+
+    @testset "NNConv" begin
+        nA, nB = 2, 3
+        nedges = 6
+        g = rand_bipartite_heterograph((nA, nB), nedges;
+            edata = Dict((:A, :to, :B) => (e = rand(Float32, 5, nedges),),
+                        (:B, :to, :A) => (e = rand(Float32, 5, nedges),))
+        )
+        x = (A = rand(Float32, 4, nA), B = rand(Float32, 4, nB))
+        nn = Dense(5 => 4 * 2)
+        layers = HeteroGraphConv((:A, :to, :B) => NNConv(4 => 2, nn, tanh),
+                                 (:B, :to, :A) => NNConv(4 => 2, nn, tanh))
+        y = layers(g, x)
+        @test size(y.A) == (2, nA) && size(y.B) == (2, nB)
+    end
 end