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Documentation with MultiDocumenter.jl (#508)
* New docs * Update * Actions * Fix branch * Fix * Change path * Tentative * Fix * Some checks * Add Graphs interlink * FIx * ok * fix? * update julia version * Change a bit * dev GNNGraphs * Fix path * Test * Add envs * Rewrite GraphNeuralNetwork part * Fix typo * Remove old docs * Test branch docs * Other test * Another test * Test * New test * Back to prevous path * Back to git error * As Datatoolkit * Change name * remove * Locally works * Test push in branch * Add check * Change repo * Test GNNGraphs deploy * Fix spaces * Fix workflows * Remove https from repo paths * Fix datasets * Fixes * Remove git ignore * Test url * Back to previous urls and update yml * Fix * test yml * Later * Fix L * Test as multi * Update gitignore * Test orphan branch * Add git config * Add checks * Stash * drop stash useless * Force checkout * Add restore * Check what is deleting * Other check to understand * It was deleting docs * Fix typo * Fix name * Cleaner * Clean to be merge in master * Fix branch switch to master
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‎.github/workflows/docs.yml‎

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‎.github/workflows/multidocs.yml‎

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name: MultiDocumentation
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on:
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push:
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branches:
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- master
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tags: '*'
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pull_request:
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jobs:
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build_multidocs:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: julia-actions/setup-julia@v2
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with:
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version: '1.10.5'
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- uses: julia-actions/cache@v2
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- name: Set up
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run: git config --global init.defaultBranch master
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# Build GNNGraphs docs
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- name: Install dependencies for GNNGraphs
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run:
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julia --project=GNNGraphs/docs/ -e '
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using Pkg;
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Pkg.develop(PackageSpec(path=joinpath(pwd(), "GNNGraphs")));
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Pkg.instantiate();'
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- name: Build GNNGraphs docs
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
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run: julia --project=GNNGraphs/docs/ GNNGraphs/docs/make.jl
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# Build GNNlib docs
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- name: Install dependencies for GNNlib
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run: julia --project=GNNlib/docs/ -e 'using Pkg; Pkg.instantiate();'
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- name: Build GNNlib docs
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
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run: julia --project=GNNlib/docs/ GNNlib/docs/make.jl
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# Build GNNLux docs
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- name: Install dependencies for GNNLux
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run: julia --project=GNNLux/docs/ -e '
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using Pkg;
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Pkg.develop(PackageSpec(path=joinpath(pwd(), "GNNLux")));
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Pkg.instantiate();'
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- name: Build GNNLux docs
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
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run: julia --project=GNNLux/docs/ GNNLux/docs/make.jl
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# Build GraphNeuralNetworks docs
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- name: Install dependencies for GraphNeuralNetworks
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run: julia --project=GraphNeuralNetworks/docs/ -e '
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using Pkg;
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Pkg.develop(PackageSpec(path=joinpath(pwd(), "GraphNeuralNetworks")));
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Pkg.instantiate();'
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- name: Build GraphNeuralNetworks docs
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
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run: julia --project=GraphNeuralNetworks/docs/ GraphNeuralNetworks/docs/make.jl
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# Build multidocs
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- name: Install dependencies for main docs
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run: julia --project=GraphNeuralNetworks/docs/ -e '
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using Pkg;
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Pkg.develop(PackageSpec(path=joinpath(pwd(), "GraphNeuralNetworks")));
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Pkg.instantiate();'
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- name: Build main docs
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
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run: julia --project=GraphNeuralNetworks/docs/make.jl
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# Build tutorials
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- name: Install dependencies for tutorials
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run: julia --project=tutorials/docs/ -e 'using Pkg; Pkg.instantiate();'
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- name: Build tutorials
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
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run: julia --project=tutorials/docs/ tutorials/docs/make.jl
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# Build and deploy multidocs
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- name: Install dependencies for multidocs
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run: julia --project=docs/ -e '
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using Pkg;
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Pkg.develop([PackageSpec(path=joinpath(pwd(), "GraphNeuralNetworks")),
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PackageSpec(path=joinpath(pwd(), "GNNGraphs")),
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PackageSpec(path=joinpath(pwd(), "GNNlib")),
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PackageSpec(path=joinpath(pwd(), "GNNLux"))]);
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Pkg.instantiate();'
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- name: Check if objects.inv exists for GraphNeuralNetworks
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run: |
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if [ -f GraphNeuralNetworks/docs/build/objects.inv ]; then
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echo "GraphNeuralNetworks: objects.inv exists."
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else
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echo "GraphNeuralNetworks: objects.inv does not exist!" && exit 1
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fi
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- name: Build and deploy multidocs
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
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run: |
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git config user.name github-actions
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git config user.email [email protected]
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julia --project=docs/ docs/make-multi.jl

‎.gitignore‎

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*.swp
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*.swo
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Manifest.toml
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/docs/build/
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.vscode
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LocalPreferences.toml
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.DS_Store
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docs/src/democards/gridtheme.css
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test.jl
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test.jl
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docs/build
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GNNGraphs/docs/build
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GNNlib/docs/build
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GNNLux/docs/build
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GraphNeuralNetworks/docs/build
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tutorials/docs/build

‎GNNGraphs/docs/Project.toml‎

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[deps]
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
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GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
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Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
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LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"

‎GNNGraphs/docs/make.jl‎

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using Documenter
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using DocumenterInterLinks
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using GNNGraphs
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import Graphs
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using Graphs: induced_subgraph
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assets=[]
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prettyurls = get(ENV, "CI", nothing) == "true"
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mathengine = MathJax3()
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makedocs(;
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modules = [GNNGraphs],
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doctest = false,
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clean = true,
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format = Documenter.HTML(; mathengine, prettyurls, assets = assets, size_threshold=nothing),
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sitename = "GNNGraphs.jl",
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pages = ["Home" => "index.md",
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"Graphs" => ["gnngraph.md", "heterograph.md", "temporalgraph.md"],
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"Datasets" => "datasets.md",
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"API Reference" => [
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"GNNGraph" => "api/gnngraph.md",
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"GNNHeteroGraph" => "api/heterograph.md",
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"TemporalSnapshotsGNNGraph" => "api/temporalgraph.md",
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],
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]
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)
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deploydocs(;repo = "github.com/JuliaGraphs/GraphNeuralNetworks.jl.git",
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devbranch = "master",
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push_preview = true,
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dirname = "GNNGraphs")

GraphNeuralNetworks/docs/src/api/gnngraph.md renamed to GNNGraphs/docs/src/api/gnngraph.md

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# GNNGraph
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Documentation page for the graph type `GNNGraph` provided by GraphNeuralNetworks.jl and related methods.
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Documentation page for the graph type `GNNGraph` provided by GNNGraphs.jl and related methods.
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Besides the methods documented here, one can rely on the large set of functionalities
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given by [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl) thanks to the fact

‎GNNGraphs/docs/src/api/heterograph.md‎

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# Heterogeneous Graphs
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## GNNHeteroGraph
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Documentation page for the type `GNNHeteroGraph` representing heterogeneous graphs, where nodes and edges can have different types.
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```@autodocs
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Modules = [GNNGraphs]
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Pages = ["gnnheterograph.jl"]
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Private = false
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```
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```@docs
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Graphs.has_edge(::GNNHeteroGraph, ::Tuple{Symbol, Symbol, Symbol}, ::Integer, ::Integer)
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```
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‎GNNGraphs/docs/src/datasets.md‎

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# Datasets
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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/JuliaGraphs/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/).
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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.
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GraphNeuralNetworks.jl provides the [`mldataset2gnngraph`](@ref) method for interfacing with MLDatasets.jl.
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```@docs
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mldataset2gnngraph
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```

GraphNeuralNetworks/docs/src/gnngraph.md renamed to GNNGraphs/docs/src/gnngraph.md

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# Working with GNNGraph
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# Static Graphs
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The fundamental graph type in GraphNeuralNetworks.jl is the [`GNNGraph`](@ref).
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The fundamental graph type in GNNGraphs.jl is the [`GNNGraph`](@ref).
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A GNNGraph `g` is a directed graph with nodes labeled from 1 to `g.num_nodes`.
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The underlying implementation allows for efficient application of graph neural network
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A GNNGraph can be created from several different data sources encoding the graph topology:
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```julia
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using GraphNeuralNetworks, Graphs, SparseArrays
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using GNNGraphs, Graphs, SparseArrays
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## Edge weights
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It is common to denote scalar edge features as edge weights. The `GNNGraph` has specific support
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for edge weights: they can be stored as part of internal representations of the graph (COO or adjacency matrix). Some graph convolutional layers, most notably the [`GCNConv`](@ref), can use the edge weights to perform weighted sums over the nodes' neighborhoods.
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for edge weights: they can be stored as part of internal representations of the graph (COO or adjacency matrix). Some graph convolutional layers, most notably the `GCNConv`, can use the edge weights to perform weighted sums over the nodes' neighborhoods.
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```julia
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```julia
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julia> using GraphNeuralNetworks
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julia> using GNNGraphs
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# A Graphs.jl undirected graph
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julia> gu = Graphs.erdos_renyi(10, 20)

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