From 4fed7e8b01ee317c7c3b03383a8b10113fb22aaa Mon Sep 17 00:00:00 2001
From: rbSparky <rishb369@gmail.com>
Date: Fri, 15 Apr 2022 11:46:20 +0530
Subject: [PATCH 1/3] Added main code

---
 src/GraphNeuralNetworks.jl |  1 +
 src/layers/conv.jl         | 66 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 67 insertions(+)

diff --git a/src/GraphNeuralNetworks.jl b/src/GraphNeuralNetworks.jl
index 11c3e4039..a680f77aa 100644
--- a/src/GraphNeuralNetworks.jl
+++ b/src/GraphNeuralNetworks.jl
@@ -61,6 +61,7 @@ export
     ResGatedGraphConv,
     SAGEConv,
     GMMConv,
+    EdgeWeightNorm,
     
     # layers/pool
     GlobalPool,
diff --git a/src/layers/conv.jl b/src/layers/conv.jl
index 7bfbe9592..62a2123bc 100644
--- a/src/layers/conv.jl
+++ b/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

From 7ca7589aaec43bd2189fd068599ef83f66b9a66b Mon Sep 17 00:00:00 2001
From: Rishabh B <59335537+rbSparky@users.noreply.github.com>
Date: Fri, 15 Apr 2022 11:57:04 +0530
Subject: [PATCH 2/3] Small doc fix

---
 src/layers/conv.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/layers/conv.jl b/src/layers/conv.jl
index 62a2123bc..25da7454f 100644
--- a/src/layers/conv.jl
+++ b/src/layers/conv.jl
@@ -1195,7 +1195,7 @@ 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.
+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.
 

From 71f5455bed75ea058f782ec0e94268cd70cf650c Mon Sep 17 00:00:00 2001
From: rbSparky <rishb369@gmail.com>
Date: Tue, 3 May 2022 10:15:59 +0530
Subject: [PATCH 3/3] Changed output type to be same as edge_weight

---
 src/layers/conv.jl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/layers/conv.jl b/src/layers/conv.jl
index 62a2123bc..80b4058e8 100644
--- a/src/layers/conv.jl
+++ b/src/layers/conv.jl
@@ -1230,8 +1230,8 @@ function EdgeWeightNorm(norm_both::Bool = true,
     EdgeWeightNorm(norm_both, eps)
 end
 
-function (l::EdgeWeightNorm)(g::GNNGraph, edge_weight::AbstractVector)
-    norm_val = Vector{Float64}()    
+function (l::EdgeWeightNorm)(g::GNNGraph, edge_weight::T) where T <: AbstractVector
+    norm_val = T()    
     edge_in, edge_out = edge_index(g)
     
     dg_in = degree(g; dir = :in, edge_weight)