Add EdgeConv docstring

Author: aurorarossi

GNNLux/src/layers/conv.jl

@@ -517,6 +517,49 @@ function (l::CGConv)(g, x, e, ps, st)
     return GNNlib.cg_conv(m, g, x, e), st
 end
 
+@doc raw"""
+    EdgeConv(nn; aggr=max)
+
+Edge convolutional layer from paper [Dynamic Graph CNN for Learning on Point Clouds](https://arxiv.org/abs/1801.07829).
+
+Performs the operation
+```math
+\mathbf{x}_i' = \square_{j \in N(i)}\, nn([\mathbf{x}_i; \mathbf{x}_j - \mathbf{x}_i])
+```
+
+where `nn` generally denotes a learnable function, e.g. a linear layer or a multi-layer perceptron.
+
+# Arguments
+
+- `nn`: A (possibly learnable) function. 
+- `aggr`: Aggregation operator for the incoming messages (e.g. `+`, `*`, `max`, `min`, and `mean`).
+
+# 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]
+in_channel = 3
+out_channel = 5
+g = GNNGraph(s, t)
+x = rand(rng, Float32, in_channel, g.num_nodes)
+
+# create layer
+l = EdgeConv(Dense(2 * in_channel, out_channel), aggr = +)
+
+# setup layer
+ps, st = LuxCore.setup(rng, l)
+
+# forward pass
+y, st = l(g, x, ps, st)
+```
+"""
 @concrete struct EdgeConv <: GNNContainerLayer{(:nn,)}
     nn <: AbstractLuxLayer
     aggr