JuliaGraphs · aurorarossi · Sep 17, 2024 · Sep 18, 2024 · Sep 18, 2024 · Sep 18, 2024
diff --git a/.gitignore b/.gitignore
@@ -10,4 +10,9 @@ Manifest.toml
 LocalPreferences.toml
 .DS_Store
 docs/src/democards/gridtheme.css
-test.jl
+test.jl
+/GNNGraphs/docs/build/
+/GNNlib/docs/build/
+/GNNLux/docs/build/
+/multidocs/build/
+/tutorials/docs/build/
diff --git a/GNNGraphs/docs/Project.toml b/GNNGraphs/docs/Project.toml
@@ -0,0 +1,6 @@
+[deps]
+Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
+GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"
diff --git a/GNNGraphs/docs/make.jl b/GNNGraphs/docs/make.jl
@@ -0,0 +1,37 @@
+using Documenter
+using DocumenterInterLinks
+using GNNGraphs
+using Graphs
+
+assets=[]
+prettyurls = get(ENV, "CI", nothing) == "true"
+mathengine = MathJax3()
+
+interlinks = InterLinks(
+    "GraphNeuralNetworks" => ("https://carlolucibello.github.io/GraphNeuralNetworks.jl/graphneuralnetworks/",  joinpath(dirname(dirname(@__DIR__)), "docs", "build", "objects.inv")),
+
+   )
+
+makedocs(;
+         modules = [GNNGraphs],
+         doctest = false,
+         clean = true,
+         plugins = [interlinks],
+         format = Documenter.HTML(; mathengine, prettyurls, assets = assets, size_threshold=nothing),
+         sitename = "GNNGraphs.jl",
+         pages = ["Home" => "index.md",
+            "Graphs" => ["gnngraph.md", "heterograph.md", "temporalgraph.md"],
+            "Datasets" => "datasets.md",
+            "API Reference" => [
+                 "GNNGraph" => "api/gnngraph.md",
+                 "GNNHeteroGraph" => "api/heterograph.md",
+                 "TemporalSnapshotsGNNGraph" => "api/temporalgraph.md",
+              ],      
+         ]
+         )
+
+
+
+
+deploydocs(;repo = "https://github.com/CarloLucibello/GraphNeuralNetworks.jl.git",
+dirname = "GNNGraphs")
diff --git a/docs/src/api/gnngraph.md → GNNGraphs/docs/src/api/gnngraph.md b/docs/src/api/gnngraph.md → GNNGraphs/docs/src/api/gnngraph.md
@@ -4,7 +4,7 @@ CurrentModule = GNNGraphs
 
 # GNNGraph
 
-Documentation page for the graph type `GNNGraph` provided by GraphNeuralNetworks.jl and related methods. 
+Documentation page for the graph type `GNNGraph` provided by GNNGraphs.jl and related methods. 
 
 Besides the methods documented here, one can rely on the large set of functionalities
 given by [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl) thanks to the fact

diff --git a/GNNGraphs/docs/src/api/heterograph.md b/GNNGraphs/docs/src/api/heterograph.md
@@ -0,0 +1,17 @@
+# Heterogeneous Graphs
+
+
+## GNNHeteroGraph
+Documentation page for the type `GNNHeteroGraph` representing heterogeneous graphs, where  nodes and edges can have different types.
+
+
+```@autodocs
+Modules = [GNNGraphs]
+Pages   = ["gnnheterograph.jl"]
+Private = false
+```
+
+```@docs
+Graphs.has_edge(::GNNHeteroGraph, ::Tuple{Symbol, Symbol, Symbol}, ::Integer, ::Integer)
+```
+
diff --git a/docs/src/api/temporalgraph.md → GNNGraphs/docs/src/api/temporalgraph.md b/docs/src/api/temporalgraph.md → GNNGraphs/docs/src/api/temporalgraph.md
diff --git a/GNNGraphs/docs/src/datasets.md b/GNNGraphs/docs/src/datasets.md
@@ -0,0 +1,10 @@
+# Datasets
+
+GNNGraphs.jl doesn't come with its own datasets, but leverages those available in the Julia (and non-Julia) ecosystem. In particular, the [examples in the GraphNeuralNetworks.jl repository](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/tree/master/examples) make use of the [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl) package. There you will find common graph datasets such as Cora, PubMed, Citeseer, TUDataset and [many others](https://juliaml.github.io/MLDatasets.jl/dev/datasets/graphs/).
+For graphs with static structures and temporal features, datasets such as METRLA, PEMSBAY, ChickenPox, and WindMillEnergy are available. For graphs featuring both temporal structures and temporal features, the TemporalBrains dataset is suitable.
+
+GraphNeuralNetworks.jl provides the [`mldataset2gnngraph`](@ref) method for interfacing with MLDatasets.jl.
+
+```@docs
+mldataset2gnngraph
+```
diff --git a/docs/src/gnngraph.md → GNNGraphs/docs/src/gnngraph.md b/docs/src/gnngraph.md → GNNGraphs/docs/src/gnngraph.md
@@ -1,6 +1,6 @@
-# Working with GNNGraph 
+# Static Graphs 
 
-The fundamental graph type in GraphNeuralNetworks.jl is the [`GNNGraph`](@ref).
+The fundamental graph type in GNNGraphs.jl is the [`GNNGraph`](@ref).
 A GNNGraph `g` is a directed graph with nodes labeled from 1 to `g.num_nodes`.
 The underlying implementation allows for efficient application of graph neural network
 operators, gpu movement, and storage of node/edge/graph related feature arrays.
@@ -12,7 +12,7 @@ therefore it supports most functionality from that library.
 A GNNGraph can be created from several different data sources encoding the graph topology:
 
 ```julia
-using GraphNeuralNetworks, Graphs, SparseArrays
+using GNNGraphs, Graphs, SparseArrays
 
 
 # Construct a GNNGraph from from a Graphs.jl's graph
@@ -233,7 +233,7 @@ Moreover, a `GNNGraph` can be easily constructed from a `Graphs.Graph` or a `Gra
 ```julia
 julia> import Graphs
 
-julia> using GraphNeuralNetworks
+julia> using GNNGraphs
 
 # A Graphs.jl undirected graph
 julia> gu = Graphs.erdos_renyi(10, 20)    

diff --git a/docs/src/heterograph.md → GNNGraphs/docs/src/heterograph.md b/docs/src/heterograph.md → GNNGraphs/docs/src/heterograph.md
@@ -6,7 +6,7 @@ Relations such as `:rate` or `:like` can connect nodes of different types. We ca
 
 Different node/edge types can store different groups of features
 and this makes heterographs a very flexible modeling tools 
-and data containers. In GraphNeuralNetworks.jl heterographs are implemented in 
+and data containers. In GNNGraphs.jl heterographs are implemented in 
 the type [`GNNHeteroGraph`](@ref).
 
 

diff --git a/GNNGraphs/docs/src/index.md b/GNNGraphs/docs/src/index.md
@@ -0,0 +1,15 @@
+# GNNGraphs.jl
+
+GNNGraphs.jl is a package that provides graph data structures and helper functions specifically designed for working with graph neural networks. This package allows to store not only the graph structure, but also features associated with nodes, edges, and the graph itself. It is the core foundation for the GNNlib, GraphNeuralNetworks, and GNNLux packages.
+
+It supports three types of graphs: 
+
+- **Static graph** is the basic graph type represented by [`GNNGraph`](@ref), where each node and edge can have associated features. This type of graph is used in typical graph neural network applications, where neural networks operate on both the structure of the graph and the features stored in it. It can be used to represent a graph where the structure does not change over time, but the features of the nodes and edges can change over time.
+
+- **Heterogeneous graph** is a graph that supports multiple types of nodes and edges, and is represented by [`GNNHeteroGraph`](@ref). Each type can have its own properties and relationships. This is useful in scenarios with different entities and interactions, such as in citation graphs or multi-relational data.
+
+- **Temporal graph** is a graph that changes over time, and is represented by [`TemporalSnapshotsGNNGraph`](@ref). Edges and features can change dynamically. This type of graph is useful for applications that involve tracking time-dependent relationships, such as social networks.
+
+
+
+This package depends on the package [Graphs.jl] (https://github.com/JuliaGraphs/Graphs.jl).
diff --git a/docs/src/temporalgraph.md → GNNGraphs/docs/src/temporalgraph.md b/docs/src/temporalgraph.md → GNNGraphs/docs/src/temporalgraph.md
@@ -1,6 +1,6 @@
 # Temporal Graphs
 
-Temporal Graphs are graphs with time varying topologies and node features. In GraphNeuralNetworks.jl temporal graphs with fixed number of nodes over time are supported by the [`TemporalSnapshotsGNNGraph`](@ref) type.
+Temporal Graphs are graphs with time varying topologies and  features. In GNNGraphs.jl, temporal graphs with fixed number of nodes over time are supported by the [`TemporalSnapshotsGNNGraph`](@ref) type.
 
 ## Creating a TemporalSnapshotsGNNGraph
 
@@ -124,10 +124,10 @@ Vector{Matrix{Float64}}
 
 ## Graph convolutions on TemporalSnapshotsGNNGraph
 
-A graph convolutional layer can be applied to each snapshot independently, in the next example we apply a `GINConv` layer to each snapshot of a `TemporalSnapshotsGNNGraph`. The list of compatible graph convolution layers can be found [here](api/conv.md). 
+A graph convolutional layer can be applied to each snapshot independently, in the next example we apply a [`GINConv`](@ref) layer to each snapshot of a `TemporalSnapshotsGNNGraph`.  
 
 ```jldoctest
-julia> using GraphNeuralNetworks, Flux
+julia> using GNNGraphs, Flux
 
 julia> snapshots = [rand_graph(10, 20; ndata = rand(3, 10)), rand_graph(10, 14; ndata = rand(3, 10))];
 

diff --git a/GNNGraphs/src/gnngraph.jl b/GNNGraphs/src/gnngraph.jl
@@ -66,7 +66,7 @@ functionality from that library.
 # Examples
 
 ```julia
-using GraphNeuralNetworks
+using GNNGraphs, Graphs
 
 # Construct from adjacency list representation
 data = [[2,3], [1,4,5], [1], [2,5], [2,4]]

diff --git a/GNNGraphs/src/gnnheterograph.jl b/GNNGraphs/src/gnnheterograph.jl
@@ -37,7 +37,7 @@ It is similar to [`GNNGraph`](@ref) but nodes and edges are of different types.
 # Examples
 
 ```julia
-julia> using GraphNeuralNetworks
+julia> using GNNGraphs
 
 julia> nA, nB = 10, 20;
 

diff --git a/GNNGraphs/src/mldatasets.jl b/GNNGraphs/src/mldatasets.jl
@@ -7,8 +7,8 @@ Convert a graph dataset from the package MLDatasets.jl into one or many [`GNNGra
 
 # Examples
 
-```jldoctest
-julia> using MLDatasets, GraphNeuralNetworks
+```julia
+julia> using MLDatasets, GNNGraphs
 
 julia> mldataset2gnngraph(Cora())
 GNNGraph:

diff --git a/GNNGraphs/src/temporalsnapshotsgnngraph.jl b/GNNGraphs/src/temporalsnapshotsgnngraph.jl
@@ -14,7 +14,7 @@ The features can be passed at construction time or added later.
 # Examples
 
 ```julia
-julia> using GraphNeuralNetworks
+julia> using GNNGraphs
 
 julia> snapshots = [rand_graph(10,20) for i in 1:5];
 
@@ -78,7 +78,7 @@ Return a `TemporalSnapshotsGNNGraph` created starting from `tg` by adding the sn
 # Examples
 
 ```jldoctest
-julia> using GraphNeuralNetworks
+julia> using GNNGraphs
 
 julia> snapshots = [rand_graph(10, 20) for i in 1:5];
 
@@ -138,7 +138,7 @@ Return a [`TemporalSnapshotsGNNGraph`](@ref) created starting from `tg` by remov
 # Examples
 
 ```jldoctest
-julia> using GraphNeuralNetworks
+julia> using GNNGraphs
 
 julia> snapshots = [rand_graph(10,20), rand_graph(10,14), rand_graph(10,22)];
 

diff --git a/GNNGraphs/src/transform.jl b/GNNGraphs/src/transform.jl
@@ -170,7 +170,7 @@ A new GNNGraph with the specified edges removed.
 
 # Example
 ```julia
-julia> using GraphNeuralNetworks
+julia> using GNNGraphs
 
 # Construct a GNNGraph
 julia> g = GNNGraph([1, 1, 2, 2, 3], [2, 3, 1, 3, 1])
@@ -275,7 +275,7 @@ A new GNNGraph with the specified nodes and all edges associated with these node
 
 # Example
 ```julia
-using GraphNeuralNetworks
+using GNNGraphs
 
 g = GNNGraph([1, 1, 2, 2, 3], [2, 3, 1, 3, 1])
 

diff --git a/GNNLux/docs/Project.toml b/GNNLux/docs/Project.toml
@@ -0,0 +1,5 @@
+[deps]
+Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+GNNLux = "e8545f4d-a905-48ac-a8c4-ca114b98986d"
+GNNlib = "a6a84749-d869-43f8-aacc-be26a1996e48"
+LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"
diff --git a/GNNLux/docs/make.jl b/GNNLux/docs/make.jl
@@ -0,0 +1,26 @@
+using Documenter
+using GNNlib
+using GNNLux
+
+
+
+assets=[]
+prettyurls = get(ENV, "CI", nothing) == "true"
+mathengine = MathJax3()
+
+
+makedocs(;
+         modules = [GNNLux],
+         doctest = false,
+         clean = true,
+         format = Documenter.HTML(; mathengine, prettyurls, assets = assets, size_threshold=nothing),
+         sitename = "GNNLux.jl",
+         pages = ["Home" => "index.md",
+         "Basic" => "api/basic.md"],
+         )
+
+
+
+
+deploydocs(;repo = "https://github.com/CarloLucibello/GraphNeuralNetworks.jl.git",
+dirname = "GNNLux")
diff --git a/GNNLux/docs/src/api/basic.md b/GNNLux/docs/src/api/basic.md
@@ -0,0 +1,8 @@
+```@meta
+CurrentModule = GNNLux
+```
+
+## GNNLayer
+```@docs
+GNNLux.GNNLayer
+```
diff --git a/GNNLux/docs/src/index.md b/GNNLux/docs/src/index.md
@@ -0,0 +1,5 @@
+# GNNLux.jl 
+
+GNNLux.jl is a work-in-progress package that implements stateless graph convolutional layers, fully compatible with the [Lux.jl](https://lux.csail.mit.edu/stable/) machine learning framework. It is built on top of the GNNGraphs.jl, GNNlib.jl, and Lux.jl packages.
+
+The full documentation will be available soon.
diff --git a/GNNLux/src/layers/basic.jl b/GNNLux/src/layers/basic.jl
@@ -1,10 +1,10 @@
+#See also [`GNNChain`](@ref GNNLux.GNNChain).
+
 """
     abstract type GNNLayer <: AbstractLuxLayer end
 
 An abstract type from which graph neural network layers are derived.
 It is Derived from Lux's `AbstractLuxLayer` type.
-
-See also [`GNNChain`](@ref GNNLux.GNNChain).
 """
 abstract type GNNLayer <: AbstractLuxLayer end
 

diff --git a/GNNlib/docs/Project.toml b/GNNlib/docs/Project.toml
@@ -0,0 +1,6 @@
+[deps]
+Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
+GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
+GNNlib = "a6a84749-d869-43f8-aacc-be26a1996e48"
+LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"
diff --git a/GNNlib/docs/make.jl b/GNNlib/docs/make.jl
@@ -0,0 +1,41 @@
+using Documenter
+using GNNlib
+using GNNGraphs
+using DocumenterInterLinks
+
+
+assets=[]
+prettyurls = get(ENV, "CI", nothing) == "true"
+mathengine = MathJax3()
+
+interlinks = InterLinks(
+    "NNlib" => "https://fluxml.ai/NNlib.jl/stable/",
+    "GNNGraphs" => ("https://carlolucibello.github.io/GraphNeuralNetworks.jl/gnngraphs/",  joinpath(dirname(dirname(@__DIR__)), "GNNGraphs", "docs", "build", "objects.inv")),
+    "GraphNeuralNetworks" => ("https://carlolucibello.github.io/GraphNeuralNetworks.jl/graphneuralnetworks/",  joinpath(dirname(dirname(@__DIR__)), "docs", "build", "objects.inv")),)
+
+
+makedocs(;
+         modules = [GNNlib],
+         doctest = false,
+         clean = true,
+         plugins = [interlinks],
+         format = Documenter.HTML(; mathengine, prettyurls, assets = assets, size_threshold=nothing),
+         sitename = "GNNlib.jl",
+         pages = ["Home" => "index.md",
+            "Message Passing" => "messagepassing.md",
+
+            "API Reference" => [
+
+                  "Message Passing" => "api/messagepassing.md",
+
+                "Utils" => "api/utils.md",
+              ]
+
+         ]
+         )
+
+
+
+
+deploydocs(;repo = "https://github.com/CarloLucibello/GraphNeuralNetworks.jl.git",
+dirname = "GNNlib")
diff --git a/docs/src/api/messagepassing.md → GNNlib/docs/src/api/messagepassing.md b/docs/src/api/messagepassing.md → GNNlib/docs/src/api/messagepassing.md
@@ -1,5 +1,5 @@
 ```@meta
-CurrentModule = GraphNeuralNetworks
+CurrentModule = GNNlib
 ```
 
 # Message Passing

diff --git a/docs/src/api/utils.md → GNNlib/docs/src/api/utils.md b/docs/src/api/utils.md → GNNlib/docs/src/api/utils.md
@@ -1,5 +1,5 @@
 ```@meta
-CurrentModule = GraphNeuralNetworks
+CurrentModule = GNNlib
 ```
 
 # Utility Functions
@@ -17,18 +17,18 @@ Pages   = ["utils.md"]
 ### Graph-wise operations 
 
 ```@docs
-GraphNeuralNetworks.reduce_nodes
-GraphNeuralNetworks.reduce_edges
-GraphNeuralNetworks.softmax_nodes
-GraphNeuralNetworks.softmax_edges
-GraphNeuralNetworks.broadcast_nodes
-GraphNeuralNetworks.broadcast_edges
+reduce_nodes
+reduce_edges
+softmax_nodes
+softmax_edges
+broadcast_nodes
+broadcast_edges
 ```
 
 ### Neighborhood operations
 
 ```@docs
-GraphNeuralNetworks.softmax_edge_neighbors
+softmax_edge_neighbors
 ```
 
 ### NNlib