Fix

GNNLux/docs/src/api/basic.md

@@ -4,5 +4,6 @@ CurrentModule = GNNLux
 
 ## GNNLayer
 ```@docs
-GNNLux.GNNLayer
+GNNLayer
+GNNChain
 ```

GNNLux/docs/src/api/conv.md

@@ -0,0 +1,27 @@
+```@meta
+CurrentModule = GNNLux
+```
+
+# Convolutional Layers
+
+Many different types of graphs convolutional layers have been proposed in the literature. Choosing the right layer for your application could involve a lot of exploration. 
+Multiple graph convolutional layers are typically stacked together to create a graph neural network model (see [`GNNChain`](@ref)).
+
+The table below lists all graph convolutional layers implemented in the *GNNLux.jl*. It also highlights the presence of some additional capabilities with respect to basic message passing:
+- *Sparse Ops*: implements message passing as multiplication by sparse adjacency matrix instead of the gather/scatter mechanism. This can lead to better CPU performances but it is not supported on GPU yet. 
+- *Edge Weight*: supports scalar weights (or equivalently scalar features) on edges. 
+- *Edge Features*: supports feature vectors on edges.
+- *Heterograph*: supports heterogeneous graphs (see [`GNNHeteroGraph`](@ref)).
+- *TemporalSnapshotsGNNGraphs*: supports temporal graphs (see [`TemporalSnapshotsGNNGraph`](@ref)) by applying the convolution layers to each snapshot independently.
+
+| Layer                       |Sparse Ops|Edge Weight|Edge Features| Heterograph  | TemporalSnapshotsGNNGraphs |
+| :--------                   |  :---:   |:---:      |:---:        |  :---:       | :---:                      |         ✓               |
+| [`GCNConv`](@ref)           |     ✓    |     ✓     |             |       ✓      |                            |
+
+## Docs
+
+```@autodocs
+Modules = [GraphNeuralNetworks]
+Pages   = ["layers/conv.jl"]
+Private = false
+```