minor doc improve (#73)

* doc improves

Author: CarloLucibello

docs/src/api/gnngraph.md

@@ -6,14 +6,9 @@ CurrentModule = GraphNeuralNetworks
 
 Documentation page for the graph type `GNNGraph` provided by GraphNeuralNetworks.jl and related methods. 
 
-```@contents
-Pages = ["gnngraph.md"]
-Depth = 5
-```
-
 Besides the methods documented here, one can rely on the large set of functionalities
-given by [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl)
-since `GNNGraph` inherits from `Graphs.AbstractGraph`.
+given by [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl) thanks to the fact
+that `GNNGraph` inherits from `Graphs.AbstractGraph`.
 
 ## Index 
 
@@ -60,3 +55,15 @@ Modules = [GraphNeuralNetworks.GNNGraphs]
 Pages   = ["generate.jl"]
 Private = false
 ```
+
+## Operators
+
+```@autodocs
+Modules = [GraphNeuralNetworks.GNNGraphs]
+Pages   = ["operators.jl"]
+Private = false
+```
+
+```@docs
+Graphs.intersect
+```

docs/src/messagepassing.md

@@ -72,8 +72,8 @@ julia> apply_edges((xi, xj, e) -> xi.a + xi.b .* xj, g, xi=(a=x,b=z), xj=z)
  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0
  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0  5.0
 ```
-The function [@propagate](@ref) instead performs also the the [`apply_edges`](@ref) operation
-but then applies a reduction over each node's neighborhood.
+The function [`propagate`](@ref) instead performs the [`apply_edges`](@ref) operation
+but then also applies a reduction over each node's neighborhood.
 ```julia
 julia> propagate((xi, xj, e) -> xi .+ xj, g, +, xi=x, xj=z)
 2×10 Matrix{Float64}:

docs/src/models.md

@@ -1,7 +1,7 @@
 # Models
 
 GraphNeuralNetworks.jl provides common graph convolutional layers by which you can assemble arbitrarily deep or complex models. GNN layers are compatible with 
-Flux.jl ones, therefore expert Flux's users should be immediately able to define and train 
+Flux.jl ones, therefore expert Flux users are promptly able to define and train 
 their models. 
 
 In what follows, we discuss two different styles for model creation:
@@ -52,7 +52,7 @@ end
 din, d, dout = 3, 4, 2 
 model = GNN(din, d, dout)                 # step 5
 
-g = GNNGraph(random_regular_graph(10, 4))
+g = rand_graph(10, 30)
 X = randn(Float32, din, 10) 
 
 y = model(g, X)  # output size: (dout, g.num_nodes)
@@ -74,7 +74,7 @@ Using `GNNChain`, the previous example becomes
 using Flux, Graphs, GraphNeuralNetworks
 
 din, d, dout = 3, 4, 2 
-g = GNNGraph(random_regular_graph(10, 4))
+g = rand_graph(10, 30)
 X = randn(Float32, din, 10)
 
 model = GNNChain(GCNConv(din => d),
@@ -101,3 +101,25 @@ model = GNNChain( ResGatedGraphConv(din => d, relu),
 
 y = model(g, X) # output size: (dout, g.num_graphs)
 ```
+
+## Embedding a graph in the model
+
+Sometimes it is useful to consider a specific graph as a part of a model instead of 
+its input. GNN.jl provides the [`WithGraph`](@ref) type to deal with this scenario.
+
+```julia
+chain = GNNChain(GCNConv(din => d, relu),
+                 GCNConv(d => d))
+
+
+g = rand_graph(10, 30)
+
+model = WithGraph(chain, g)
+
+X = randn(Float32, din, 10)
+
+# Pass only X as input, the model already contains the graph.
+y = model(X) 
+```
+
+An example of `WithGraph` usage is given in the graph neural ODE script in the [examples](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/tree/master/examples) folder.