Added EdgeWeightNorm layer

src/GraphNeuralNetworks.jl

@@ -61,6 +61,7 @@ export
     ResGatedGraphConv,
     SAGEConv,
     GMMConv,
+    EdgeWeightNorm,
 
     # layers/pool
     GlobalPool,

src/layers/conv.jl

@@ -1181,3 +1181,69 @@ function Base.show(io::IO, l::GMMConv)
     l.residual==true || print(io, ", residual=", l.residual)
     print(io, ")")
 end
+
+@doc raw"""
+    EdgeWeightNorm(norm_both = true, eps = 0)
+
+Normalizes positive scalar edge weights on a graph following the form in GCN.
+
+norm_both = `true` yields the following normalization term:
+```math
+c_{ji} = (\sqrt{\sum_{k\in\mathcal{N}(j)}e_{jk}}\sqrt{\sum_{k\in\mathcal{N}(i)}e_{ki}})
+```
+norm_both = `false` yields the following normalization term:
+```math
+c_{ji} = (\sum_{k\in\mathcal{N}(i)}e_{ki})
+```
+where ``e_{ji}`` is the scalar weight on the edge from node j to node i.
+
+Return value is the normalized weight ``e_{ji} / c_{ji}`` for all edges in vector form.
+
+# Arguments
+
+- `norm_both`: The normalizer as specified above. Default is `true`.
+- `eps`: Offset value in the denominator. Default is `0`.
+
+# Examples
+
+```julia
+# create data
+g = GNNGraph([1,2,3,4,3,6], [2,3,4,5,1,4])
+g = add_self_loops(g)
+
+# edge weights 
+edge_weights = [0.5, 0.6, 0.4, 0.7, 0.9, 0.1, 1, 1, 1, 1, 1, 1]
+
+l = EdgeWeightNorm()
+l(g, edge_weights)
+```                    
+"""
+struct EdgeWeightNorm <: GNNLayer
+    norm_both::Bool
+    eps::Float64
+end
+
+@functor EdgeWeightNorm
+
+function EdgeWeightNorm(norm_both::Bool = true, 
+                        eps::Float64 = 0)                  
+    EdgeWeightNorm(norm_both, eps)
+end
+
+function (l::EdgeWeightNorm)(g::GNNGraph, edge_weight::AbstractVector)
+    norm_val = Vector{Float64}()    
+    edge_in, edge_out = edge_index(g)
+
+    dg_in = degree(g; dir = :in, edge_weight)
+    dg_out = degree(g; dir = :out, edge_weight)
+
+    for iter in 1:length(edge_weight)
+        if l.norm_both
+            push!(norm_val, edge_weight[iter] / (sqrt(dg_out[in[iter]] * dg_in[out[iter]]) + l.eps))
+         else
+            push!(norm_val, edge_weight[iter] / (dg_in[out[iter]] + l.eps))
+        end
+    end    
+
+    return norm_val
+end