Add SGConv

Author: aurorarossi

GNNLux/src/layers/conv.jl

@@ -1077,6 +1077,63 @@ function Base.show(io::IO, l::GATv2Conv)
     print(io, ")")
 end
 
+@doc raw"""
+    SGConv(int => out, k = 1; init_weight = glorot_uniform, init_bias = zeros32, use_bias = true, add_self_loops = true,use_edge_weight = false)
+                                
+SGC layer from [Simplifying Graph Convolutional Networks](https://arxiv.org/pdf/1902.07153.pdf)
+Performs operation
+```math
+H^{K} = (\tilde{D}^{-1/2} \tilde{A} \tilde{D}^{-1/2})^K X \Theta
+```
+where ``\tilde{A}`` is ``A + I``.
+
+# Arguments
+
+- `in`: Number of input features.
+- `out`: Number of output features.
+- `k` : Number of hops k. Default `1`.
+- `add_self_loops`: Add self loops to the graph before performing the convolution. Default `false`.
+- `use_edge_weight`: If `true`, consider the edge weights in the input graph (if available).
+                     If `add_self_loops=true` the new weights will be set to 1. Default `false`.
+- `init_weight`: Weights' initializer. Default `glorot_uniform`.
+- `init_bias`: Bias initializer. Default `zeros32`.
+- `use_bias`: Add learnable bias. Default `true`.
+
+
+# Examples
+
+```julia
+using GNNLux, Lux, Random
+
+# initialize random number generator
+rng = Random.default_rng()
+
+# create data
+s = [1,1,2,3]
+t = [2,3,1,1]
+g = GNNGraph(s, t)
+x = randn(rng, Float32, 3, g.num_nodes)
+
+# create layer
+l = SGConv(3 => 5; add_self_loops = true) 
+
+# setup layer
+ps, st = LuxCore.setup(rng, l)
+
+# forward pass
+y, st = l(g, x, ps, st)       # size:  5 × num_nodes
+
+# convolution with edge weights
+w = [1.1, 0.1, 2.3, 0.5]
+y = l(g, x, w, ps, st)
+
+# Edge weights can also be embedded in the graph.
+g = GNNGraph(s, t, w)
+l = SGConv(3 => 5, add_self_loops = true, use_edge_weight=true) 
+ps, st = LuxCore.setup(rng, l)
+y, st = l(g, x, ps, st) # same as l(g, x, w) 
+```
+"""
 @concrete struct SGConv <: GNNLayer
     in_dims::Int
     out_dims::Int