refactor GNNGraph into submodule + implement add_edges

Project.toml

@@ -9,6 +9,7 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 LearnBase = "7f8f8fb0-2700-5f03-b4bd-41f8cfc144b6"
@@ -17,6 +18,7 @@ MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 NNlibCUDA = "a00861dc-f156-4864-bf3c-e6376f28a68d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
@@ -28,12 +30,14 @@ CUDA = "3.3"
 ChainRulesCore = "1"
 DataStructures = "0.18"
 Flux = "0.12.7"
+Functors = "0.2"
 Graphs = "1.4"
 KrylovKit = "0.5"
 LearnBase = "0.4, 0.5"
 MacroTools = "0.5"
 NNlib = "0.7"
 NNlibCUDA = "0.1"
+Reexport = "1"
 StatsBase = "0.32, 0.33"
 TestEnv = "1"
 julia = "1.6"

docs/make.jl

@@ -1,7 +1,9 @@
 using Flux, NNlib, GraphNeuralNetworks, Graphs, SparseArrays
 using Documenter
 
-DocMeta.setdocmeta!(GraphNeuralNetworks, :DocTestSetup, :(using GraphNeuralNetworks); recursive=true)
+DocMeta.setdocmeta!(GraphNeuralNetworks, :DocTestSetup, 
+    :(using GraphNeuralNetworks, Graphs, SparseArrays, NNlib, Flux); 
+    recursive=true)
 
 makedocs(;
     modules=[GraphNeuralNetworks, NNlib, Flux, Graphs, SparseArrays],

docs/src/api/gnngraph.md

@@ -4,7 +4,12 @@ CurrentModule = GraphNeuralNetworks
 
 # GNNGraph
 
-Documentation page for the graph type `GNNGraph` provided GraphNeuralNetworks.jl and its related methods. 
+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)
@@ -17,16 +22,41 @@ Order = [:type, :function]
 Pages   = ["gnngraph.md"]
 ```
 
-## Docs
+## GNNGraph type
 
 ```@autodocs
-Modules = [GraphNeuralNetworks]
+Modules = [GraphNeuralNetworks.GNNGraphs]
 Pages   = ["gnngraph.jl"]
 Private = false
 ```
 
+## Query
+
+```@autodocs
+Modules = [GraphNeuralNetworks.GNNGraphs]
+Pages   = ["query.jl"]
+Private = false
+```
+
 ```@docs
-Flux.batch
-SparseArrays.blockdiag
 Graphs.adjacency_matrix
+Graphs.degree
+Graphs.outneighbors
+Graphs.inneighbors
+```
+
+## Transform
+
+```@autodocs
+Modules = [GraphNeuralNetworks.GNNGraphs]
+Pages   = ["transform.jl"]
+Private = false
+```
+
+## Generate
+
+```@autodocs
+Modules = [GraphNeuralNetworks.GNNGraphs]
+Pages   = ["generate.jl"]
+Private = false
 ```

docs/src/gnngraph.md

@@ -15,10 +15,13 @@ A GNNGraph can be created from several different data sources encoding the graph
 using GraphNeuralNetworks, Graphs, SparseArrays
 
 
-# Construct GNNGraph from From Graphs's graph
+# Construct a GNNGraph from from a Graphs.jl's graph
 lg = erdos_renyi(10, 30)
 g = GNNGraph(lg)
 
+# Same as above using convenience method rand_graph
+g = rand_graph(10, 30)
+
 # From an adjacency matrix
 A = sprand(10, 10, 0.3)
 g = GNNGraph(A)
@@ -33,7 +36,7 @@ target = [2,3,1,3,1,2,4,3]
 g = GNNGraph(source, target)
 ```
 
-See also the related methods [`adjacency_matrix`](@ref), [`edge_index`](@ref), and [`adjacency_list`](@ref).
+See also the related methods [`Graphs.adjacency_matrix`](@ref), [`edge_index`](@ref), and [`adjacency_list`](@ref).
 
 ## Basic Queries
 
@@ -123,21 +126,21 @@ for g in train_loader
     .....
 end
 
-# Access the nodes' graph memberships through 
-gall.graph_indicator
+# Access the nodes' graph memberships 
+graph_indicator(gall)
 ```
 
 ## Graph Manipulation
 
 ```julia
 g′ = add_self_loops(g)
-
 g′ = remove_self_loops(g)
+g′ = add_edges(g, [1, 2], [2, 3]) # add edges 1->2 and 2->3
 ```
 
 ## JuliaGraphs ecosystem integration
 
-Since `GNNGraph <: Graphs.AbstractGraph`, we can use any functionality from Graphs. 
+Since `GNNGraph <: Graphs.AbstractGraph`, we can use any functionality from Graphs.jl. 
 
 ```julia
 @assert Graphs.isdirected(g)

src/GNNGraphs/GNNGraphs.jl

@@ -0,0 +1,43 @@
+module GNNGraphs
+
+using SparseArrays
+using Functors: @functor
+using CUDA 
+import Graphs
+using Graphs: AbstractGraph, outneighbors, inneighbors, adjacency_matrix, degree
+import Flux
+using Flux: batch
+import NNlib
+import LearnBase
+import StatsBase
+using LearnBase: getobs
+import KrylovKit
+using ChainRulesCore
+using LinearAlgebra, Random
+
+include("gnngraph.jl")
+export GNNGraph, node_features, edge_features, graph_features
+
+include("query.jl")
+export  edge_index, adjacency_list, normalized_laplacian, scaled_laplacian,
+        graph_indicator
+
+include("transform.jl")
+export add_edges, add_self_loops, remove_self_loops, getgraph
+
+include("generate.jl")
+export rand_graph
+
+
+include("convert.jl")
+include("utils.jl")
+
+export 
+    # from Graphs
+    adjacency_matrix, degree, outneighbors, inneighbors,
+    # from SparseArrays
+    sprand, sparse, blockdiag,
+    # from Flux
+    batch
+
+end #module

src/GNNGraphs/generate.jl

@@ -0,0 +1,14 @@
+"""
+    rand_graph(n, m; directed=false, kws...)
+
+Generate a random (Erdós-Renyi) `GNNGraph` with `n` nodes.
+
+If `directed=false` the output will contain `2m` edges:
+the reverse edge of each edge will be present.
+If `directed=true` instead, `m` unrelated edges are generated.
+
+Additional keyword argument  will be fed to the [`GNNGraph`](@ref) constructor.
+"""
+function rand_graph(n::Integer, m::Integer; directed=false, kws...)
+    return GNNGraph(Graphs.erdos_renyi(n, m, is_directed=directed); kws...)    
+end