layers do not store graphs internally

Author: CarloLucibello

README.md

@@ -5,9 +5,8 @@
 ![](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/actions/workflows/ci.yml/badge.svg)
 [![codecov](https://codecov.io/gh/FluxML/GraphNeuralNetworks.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/CarloLucibello/GraphNeuralNetworks.jl)
 
-GraphNeuralNetworks is a geometric deep learning library for [Flux](https://github.com/FluxML/Flux.jl). This library aims to be compatible with packages from [JuliaGraphs](https://github.com/JuliaGraphs) ecosystem and have support of CUDA GPU acceleration with [CUDA](https://github.com/JuliaGPU/CUDA.jl). Message passing scheme is implemented as a flexbile framework and fused with Graph Network block scheme. GraphNeuralNetworks is compatible with other packages that are composable with Flux.
+GraphNeuralNetworks (GNN) is a graph neural network library for Julia based on the [Flux.jl](https://github.com/FluxML/Flux.jl) deep learning framework.
 
-Suggestions, issues and pull requsts are welcome.
 
 ## Installation
 
@@ -17,15 +16,15 @@ Suggestions, issues and pull requsts are welcome.
 
 ## Features
 
-* Extend Flux deep learning framework in Julia and compatible with Flux layers.
-* Support of CUDA GPU with CUDA.jl
-* Integrate with existing JuliaGraphs ecosystem
-* Support generic graph neural network architectures
-* Variable graph inputs are supported. You use it when diverse graph structures are prepared as inputs to the same model.
+* Based on the Flux deep learning framework.
+* CUDA support.
+* Integrated with the JuliaGraphs ecosystem.
+* Supports generic graph neural network architectures.
+* Easy to define custom graph convolutional layers.
 
 ## Featured Graphs
 
-GraphNeuralNetworks handles graph data (the topology plus node/vertex/graph features)
+GraphNeuralNetworks handles graph data (the graph topology + node/edge/global features)
 thanks to the type `FeaturedGraph`.
 
 A `FeaturedGraph` can be constructed out of 
@@ -34,26 +33,45 @@ adjacency matrices, adjacency lists, LightGraphs' types...
 ```julia
 fg = FeaturedGraph(adj_list)   
 ```
+
 ## Graph convolutional layers
 
 Construct a GCN layer:
 
 ```julia
-GCNConv([fg,] input_dim => output_dim, relu)
+GCNConv(input_dim => output_dim, relu)
 ```
 
-## Use it as you use Flux
+## Usage Example
 
 ```julia
-model = Chain(GCNConv(fg, 1024 => 512, relu),
-              Dropout(0.5),
-              GCNConv(fg, 512 => 128),
-              Dense(128, 10))
-## Loss
-loss(x, y) = logitcrossentropy(model(x), y)
-accuracy(x, y) = mean(onecold(model(x)) .== onecold(y))
-
-## Training
+struct GNN
+    conv1
+    conv2 
+    dense
+
+    function GNN()
+        new(GCNConv(1024=>512, relu),
+            GCNConv(512=>128, relu), 
+            Dense(128, 10))
+    end
+end
+
+@functor GNN
+
+function (net::GNN)(g, x)
+    x = net.conv1(g, x)
+    x = dropout(x, 0.5)
+    x = net.conv2(g, x)
+    x = net.dense(x)
+    return x
+end
+
+model = GNN()
+
+loss(x, y) = logitcrossentropy(model(fg, x), y)
+accuracy(x, y) = mean(onecold(model(fg, x)) .== onecold(y))
+
 ps = Flux.params(model)
 train_data = [(train_X, train_y)]
 opt = ADAM(0.01)

src/GraphNeuralNetworks.jl

@@ -64,7 +64,6 @@ include("layers/msgpass.jl")
 
 include("layers/conv.jl")
 include("layers/pool.jl")
-include("models.jl")
 include("layers/misc.jl")
 
 

src/featuredgraph.jl

@@ -5,16 +5,7 @@ https://juliagraphs.org/LightGraphs.jl/latest/types/#AbstractGraph-Type
 https://juliagraphs.org/LightGraphs.jl/latest/developing/#Developing-Alternate-Graph-Types
 =============================================#
 
-abstract type AbstractFeaturedGraph <: AbstractGraph{Int} end
-
-"""
-    NullGraph()
-
-Null object for `FeaturedGraph`.
-"""
-struct NullGraph <: AbstractFeaturedGraph end
-
-const COO_T = Tuple{T, T, V} where {T <: AbstractVector,V}
+const COO_T = Tuple{T, T, V} where {T <: AbstractVector, V}
 const ADJLIST_T = AbstractVector{T} where T <: AbstractVector
 const ADJMAT_T = AbstractMatrix
 const SPARSE_T = AbstractSparseMatrix # subset of ADJMAT_T
@@ -93,7 +84,7 @@ source, target = edge_index(fg)
 
 See also [`graph`](@ref), [`edge_index`](@ref), [`node_feature`](@ref), [`edge_feature`](@ref), and [`global_feature`](@ref) 
 """
-struct FeaturedGraph{T<:Union{COO_T,ADJMAT_T}} <: AbstractFeaturedGraph
+struct FeaturedGraph{T<:Union{COO_T,ADJMAT_T}}
     graph::T
     num_nodes::Int
     num_edges::Int