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8 files changed

+41
-19
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GNNGraphs/docs/make.jl

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@@ -46,7 +46,8 @@ makedocs(;
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"GNNHeteroGraph" => "api/heterograph.md",
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"TemporalSnapshotsGNNGraph" => "api/temporalgraph.md",
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"Samplers" => "api/samplers.md",
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],
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"Datasets" => "api/datasets.md",
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],
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]
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)
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GNNGraphs/docs/src/api/datasets.md

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```@meta
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CurrentModule = GNNGraphs
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CollapsedDocStrings = true
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```
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# Datasets
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```@docs
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mldataset2gnngraph
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```

GNNGraphs/docs/src/api/gnngraph.md

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@@ -21,42 +21,42 @@ Base.copy
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## DataStore
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```@autodocs
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```@autodocs; canonical = true
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Modules = [GNNGraphs]
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Pages = ["datastore.jl"]
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Private = false
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```
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## Query
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```@autodocs
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```@autodocs; canonical = true
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Modules = [GNNGraphs]
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Pages = ["src/query.jl"]
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Private = false
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```
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```@docs
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```@docs; canonical = true
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Graphs.neighbors(::GNNGraph, ::Integer)
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```
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## Transform
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```@autodocs
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```@autodocs; canonical = true
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Modules = [GNNGraphs]
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Pages = ["src/transform.jl"]
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Private = false
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```
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## Utils
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```@docs
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```@docs; canonical = true
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GNNGraphs.sort_edge_index
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GNNGraphs.color_refinement
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```
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## Generate
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```@autodocs
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```@autodocs; canonical = true
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Modules = [GNNGraphs]
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Pages = ["src/generate.jl"]
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Private = false
@@ -65,24 +65,24 @@ Filter = t -> typeof(t) <: Function && t!=rand_temporal_radius_graph && t!=rand_
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## Operators
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```@autodocs
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```@autodocs; canonical = true
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Modules = [GNNGraphs]
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Pages = ["src/operators.jl"]
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Private = false
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```
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```@docs
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```@docs; canonical = true
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Base.intersect
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```
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## Sampling
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```@autodocs
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```@autodocs; canonical = true
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Modules = [GNNGraphs]
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Pages = ["src/sampling.jl"]
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Private = false
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```
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```@docs
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```@docs; canonical = true
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Graphs.induced_subgraph(::GNNGraph, ::Vector{Int})
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```

GNNGraphs/docs/src/guides/datasets.md

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@@ -5,6 +5,3 @@ For graphs with static structures and temporal features, datasets such as METRLA
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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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```

GNNLux/docs/make.jl

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@@ -68,6 +68,7 @@ makedocs(;
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"GNNHeteroGraph" => "GNNGraphs/api/heterograph.md",
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"TemporalSnapshotsGNNGraph" => "GNNGraphs/api/temporalgraph.md",
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"Samplers" => "GNNGraphs/api/samplers.md",
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"Datasets" => "GNNGraphs/api/datasets.md",
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]
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"Message Passing (GNNlib.jl)" => [

GNNlib/docs/make.jl

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@@ -7,6 +7,7 @@ Pkg.instantiate()
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using Documenter
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using GNNlib
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using GNNGraphs
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import Graphs
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using DocumenterInterLinks
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assets=[]
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"NNlib" => "https://fluxml.ai/NNlib.jl/stable/",
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)
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# Copy the docs from GNNGraphs. Will be removed at the end of the script
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cp(joinpath(@__DIR__, "../../GNNGraphs/docs/src/"),
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joinpath(@__DIR__, "src/GNNGraphs/"), force=true)
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makedocs(;
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modules = [GNNlib],
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doctest = false,
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clean = true,
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modules = [GNNlib, GNNGraphs],
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doctest = false, # TODO enable doctest
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plugins = [interlinks],
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format = Documenter.HTML(; mathengine, prettyurls, assets = assets, size_threshold=nothing),
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sitename = "GNNlib.jl",
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pages = [
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"Home" => "index.md",
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"Message Passing" => "guides/messagepassing.md",
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"API Reference" => [
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"Graphs (GNNGraphs.jl)" => [
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"GNNGraph" => "GNNGraphs/api/gnngraph.md",
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"GNNHeteroGraph" => "GNNGraphs/api/heterograph.md",
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"TemporalSnapshotsGNNGraph" => "GNNGraphs/api/temporalgraph.md",
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"Samplers" => "GNNGraphs/api/samplers.md",
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"Datasets" => "GNNGraphs/api/datasets.md",
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],
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"Message Passing" => "api/messagepassing.md",
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"Utils" => "api/utils.md",
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]
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]
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)
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rm(joinpath(@__DIR__, "src/GNNGraphs"), force=true, recursive=true)
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deploydocs(
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repo = "github.com/JuliaGraphs/GraphNeuralNetworks.jl",
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target = "build",

GNNlib/src/msgpass.jl

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@@ -22,7 +22,7 @@ providing as input `f` a closure.
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- `g`: A `GNNGraph`.
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- `xi`: An array or a named tuple containing arrays whose last dimension's size
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is `g.num_nodes`. It will be appropriately materialized on the
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target node of each edge (see also [`edge_index`](@ref)).
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target node of each edge (see also [`edge_index`](@ref GNNGraphs.edge_index)).
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- `xj`: As `xj`, but to be materialized on edges' sources.
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- `e`: An array or a named tuple containing arrays whose last dimension's size is `g.num_edges`.
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- `fmsg`: A generic function that will be passed over to [`apply_edges`](@ref).
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- `g`: An `AbstractGNNGraph`.
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- `xi`: An array or a named tuple containing arrays whose last dimension's size
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is `g.num_nodes`. It will be appropriately materialized on the
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target node of each edge (see also [`edge_index`](@ref)).
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target node of each edge (see also [`edge_index`](@ref GNNGraphs.edge_index)).
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- `xj`: As `xi`, but now to be materialized on each edge's source node.
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- `e`: An array or a named tuple containing arrays whose last dimension's size is `g.num_edges`.
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- `fmsg`: A function that takes as inputs the edge-materialized `xi`, `xj`, and `e`.

GraphNeuralNetworks/docs/make.jl

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@@ -74,6 +74,7 @@ makedocs(;
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"GNNHeteroGraph" => "GNNGraphs/api/heterograph.md",
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"TemporalSnapshotsGNNGraph" => "GNNGraphs/api/temporalgraph.md",
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"Samplers" => "GNNGraphs/api/samplers.md",
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"Datasets" => "GNNGraphs/api/datasets.md",
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]
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"Message Passing (GNNlib.jl)" => [

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