Add EvolveGNCO docs

Author: aurorarossi

GNNLux/src/layers/temporalconv.jl

@@ -480,6 +480,44 @@ y, st = l(g, x, ps, st)      # result size (5, 5)
 """
 DCGRU(ch::Pair{Int, Int}, k::Int; kwargs...) = GNNLux.StatefulRecurrentCell(DCGRUCell(ch, k; kwargs...))
 
+"""
+    EvolveGCNO(ch; use_bias = true, init_weight = glorot_uniform, init_state = zeros32, init_bias = zeros32)
+
+Evolving Graph Convolutional Network (EvolveGCNO) layer from the paper [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://arxiv.org/pdf/1902.10191).
+
+Perfoms a Graph Convolutional layer with parameters derived from a Long Short-Term Memory (LSTM) layer across the snapshots of the temporal graph.
+
+
+# Arguments
+
+- `in`: Number of input features.
+- `out`: Number of output features.
+- `use_bias`: Add learnable bias. Default `true`.
+- `init_weight`: Weights' initializer. Default `glorot_uniform`.
+- `init_state`: Initial state of the hidden stat of the GRU layer. Default `zeros32`.
+- `init_bias`: Bias initializer. Default `zeros32`.
+
+# Examples
+
+```julia
+using GNNLux, Lux, Random
+
+# initialize random number generator
+rng = Random.default_rng()
+
+# create data
+tg = TemporalSnapshotsGNNGraph([rand_graph(rng, 10, 20; ndata = rand(rng, 4, 10)), rand_graph(rng, 10, 14; ndata = rand(rng, 4, 10)), rand_graph(rng, 10, 22; ndata = rand(rng, 4, 10))])
+
+# create layer
+l = EvolveGCNO(4 => 5)
+
+# setup layer
+ps, st = LuxCore.setup(rng, l)
+
+# forward pass
+y, st = l(tg, tg.ndata.x , ps, st)      # result size 3, size y[1] (5, 10)
+```
+"""
 @concrete struct EvolveGCNO <: GNNLayer
     in_dims::Int
     out_dims::Int