fix docs (#455)

* fix docs

* fix docs

* reintroduce tutorials

* polishing

* mlutils

* workflow

* fixes

* workflow

* anouther attempt

* try again

.github/workflows/docs.yml

@@ -14,11 +14,14 @@ jobs:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@latest
         with:
-          version: '1.9.1'
+          version: '1.10.4'
       - name: Install dependencies
-        run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
+        run: julia --project=docs/ -e '
+          using Pkg;
+          Pkg.develop([PackageSpec(path=pwd()), PackageSpec(path=joinpath(pwd(), "GNNGraphs"))]);
+          Pkg.instantiate();'
       - name: Build and deploy
         env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # If authenticating with GitHub Actions token
-          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # If authenticating with SSH deploy key
-        run: julia --project=docs/ docs/make.jl
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
+        run: julia --project=docs/ docs/make.jl

.gitignore

@@ -8,4 +8,5 @@ Manifest.toml
 /docs/build/
 .vscode
 LocalPreferences.toml
-.DS_Store
+.DS_Store
+docs/src/democards/gridtheme.css

GNNGraphs/src/operators.jl

@@ -1,4 +1,9 @@
 # 2 or more args graph operators
+""""
+    intersect(g1::GNNGraph, g2::GNNGraph)
+
+Intersect two graphs by keeping only the common edges.
+"""
 function Base.intersect(g1::GNNGraph, g2::GNNGraph)
     @assert g1.num_nodes == g2.num_nodes
     @assert graph_type_symbol(g1) == graph_type_symbol(g2)

GNNGraphs/src/query.jl

@@ -89,7 +89,17 @@ Graphs.ne(g::GNNGraph) = g.num_edges
 Graphs.has_vertex(g::GNNGraph, i::Int) = 1 <= i <= g.num_nodes
 Graphs.vertices(g::GNNGraph) = 1:(g.num_nodes)
 
-function Graphs.neighbors(g::GNNGraph, i; dir = :out)
+
+"""
+    neighbors(g::GNNGraph, i::Integer; dir=:out)
+
+Return the neighbors of node `i` in the graph `g`.
+If `dir=:out`, return the neighbors through outgoing edges.
+If `dir=:in`, return the neighbors through incoming edges.
+
+See also [`outneighbors`](@ref Graphs.outneighbors), [`inneighbors`](@ref Graphs.inneighbors).
+"""
+function Graphs.neighbors(g::GNNGraph, i::Integer; dir::Symbol = :out)
     @assert dir ∈ (:in, :out)
     if dir == :out
         outneighbors(g, i)
@@ -98,6 +108,13 @@ function Graphs.neighbors(g::GNNGraph, i; dir = :out)
     end
 end
 
+"""
+    outneighbors(g::GNNGraph, i::Integer)
+
+Return the neighbors of node `i` in the graph `g` through outgoing edges.
+
+See also [`neighbors`](@ref Graphs.neighbors) and [`inneighbors`](@ref Graphs.inneighbors).
+"""
 function Graphs.outneighbors(g::GNNGraph{<:COO_T}, i::Integer)
     s, t = edge_index(g)
     return t[s .== i]
@@ -108,6 +125,13 @@ function Graphs.outneighbors(g::GNNGraph{<:ADJMAT_T}, i::Integer)
     return findall(!=(0), A[i, :])
 end
 
+"""
+    inneighbors(g::GNNGraph, i::Integer)
+
+Return the neighbors of node `i` in the graph `g` through incoming edges.
+
+See also [`neighbors`](@ref Graphs.neighbors) and [`outneighbors`](@ref Graphs.outneighbors).
+"""
 function Graphs.inneighbors(g::GNNGraph{<:COO_T}, i::Integer)
     s, t = edge_index(g)
     return s[t .== i]

GNNlib/Project.toml

@@ -12,14 +12,11 @@ Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
-MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
 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"
 
 [weakdeps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
@@ -38,14 +35,11 @@ KrylovKit = "0.6, 0.7"
 LinearAlgebra = "1"
 MLDatasets = "0.7"
 MLUtils = "0.4"
-MacroTools = "0.5"
 NNlib = "0.9"
-NearestNeighbors = "0.4"
 Random = "1"
 Reexport = "1"
 SparseArrays = "1"
 Statistics = "1"
-StatsBase = "0.34"
 cuDNN = "1"
 julia = "1.10"
 

GNNlib/src/GNNlib.jl

@@ -2,8 +2,6 @@ module GNNlib
 
 using Statistics: mean
 using LinearAlgebra, Random
-using Base: tail
-using MacroTools: @forward
 using MLUtils
 using NNlib
 using NNlib: scatter, gather

Project.toml

@@ -4,24 +4,18 @@ authors = ["Carlo Lucibello and contributors"]
 version = "0.6.19"
 
 [deps]
-Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
-Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
-KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
 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"
 
 [weakdeps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
@@ -30,26 +24,19 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 GraphNeuralNetworksCUDAExt = "CUDA"
 
 [compat]
-Adapt = "3, 4"
 CUDA = "4, 5"
 ChainRulesCore = "1"
 DataStructures = "0.18"
 Flux = "0.14"
 Functors = "0.4.1"
-Graphs = "1.4"
 GNNGraphs = "1.0"
-KrylovKit = "0.6, 0.7, 0.8"
 LinearAlgebra = "1"
-MLDatasets = "0.7"
-MLUtils = "0.4"
 MacroTools = "0.5"
+MLUtils = "0.4"
 NNlib = "0.9"
-NearestNeighbors = "0.4"
 Random = "1"
 Reexport = "1"
-SparseArrays = "1"
 Statistics = "1"
-StatsBase = "0.34"
 cuDNN = "1"
 julia = "1.10"
 
@@ -59,11 +46,13 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 InlineStrings = "842dd82b-1e85-43dc-bf29-5d0ee9dffc48"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
 [targets]
-test = ["Test", "Adapt", "DataFrames", "InlineStrings", "Zygote", "FiniteDifferences", "ChainRulesTestUtils", "MLDatasets", "CUDA", "cuDNN"]
+test = ["Test", "Adapt", "DataFrames", "InlineStrings", "SparseArrays", "Graphs", "Zygote", "FiniteDifferences", "ChainRulesTestUtils", "MLDatasets", "CUDA", "cuDNN"]

docs/Project.toml

@@ -1,12 +1,13 @@
 [deps]
 DemoCards = "311a05b2-6137-4a5a-b473-18580a3d38b5"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
 GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
-MarkdownLiteral = "736d6165-7244-6769-4267-6b50796e6954"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781"
@@ -17,4 +18,4 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 DemoCards = "0.5.0"
-Documenter = "0.27"
+Documenter = "1.5"

docs/make.jl

@@ -1,12 +1,23 @@
-using Flux, NNlib, GraphNeuralNetworks, Graphs, SparseArrays
+using GraphNeuralNetworks
+using GNNGraphs
+using Flux 
+using NNlib
+using Graphs
+using SparseArrays
 using Pluto, PlutoStaticHTML # for tutorials
 using Documenter, DemoCards
+using DocumenterInterLinks
 
-tutorials, tutorials_cb, tutorial_assets = makedemos("tutorials")
 
+tutorials, tutorials_cb, tutorial_assets = makedemos("tutorials")
 assets = []
 isnothing(tutorial_assets) || push!(assets, tutorial_assets)
 
+interlinks = InterLinks(
+    "NNlib" => "https://fluxml.ai/NNlib.jl/stable/",
+    "Graphs" => "https://juliagraphs.org/Graphs.jl/stable/")
+
+
 DocMeta.setdocmeta!(GraphNeuralNetworks, :DocTestSetup,
                     :(using GraphNeuralNetworks, Graphs, SparseArrays, NNlib, Flux);
                     recursive = true)
@@ -15,10 +26,11 @@ prettyurls = get(ENV, "CI", nothing) == "true"
 mathengine = MathJax3()
 
 makedocs(;
-         modules = [GraphNeuralNetworks, NNlib, Flux, Graphs, SparseArrays],
+         modules = [GraphNeuralNetworks, GNNGraphs],
          doctest = false,
          clean = true,
-         format = Documenter.HTML(; mathengine, prettyurls, assets = assets),
+         plugins = [interlinks],
+         format = Documenter.HTML(; mathengine, prettyurls, assets = assets, size_threshold=nothing),
          sitename = "GraphNeuralNetworks.jl",
          pages = ["Home" => "index.md",
              "Graphs" => ["gnngraph.md", "heterograph.md", "temporalgraph.md"],

docs/pluto_output/graph_classification_pluto.md

@@ -1,13 +1,13 @@
 ```@raw html
 <style>
-    table {
+    #documenter-page table {
         display: table !important;
         margin: 2rem auto !important;
         border-top: 2pt solid rgba(0,0,0,0.2);
         border-bottom: 2pt solid rgba(0,0,0,0.2);
     }
 
-    pre, div {
+    #documenter-page pre, #documenter-page div {
         margin-top: 1.4rem !important;
         margin-bottom: 1.4rem !important;
     }
@@ -25,8 +25,8 @@
 <!--
     # This information is used for caching.
     [PlutoStaticHTML.State]
-    input_sha = "f145b80b8f1e399d4cd5686b529cf173942102c538702952fe0743defca62210"
-    julia_version = "1.9.1"
+    input_sha = "62d9b08cdb51a5d174d1d090f3e4834f98df0c30b8b515e5befdd8fa22bd5c7f"
+    julia_version = "1.10.4"
 -->
 <pre class='language-julia'><code class='language-julia'>begin
     using Flux
@@ -102,7 +102,7 @@ end</code></pre>
 <div class="markdown"><p>We have some useful utilities for working with graph datasets, <em>e.g.</em>, we can shuffle the dataset and use the first 150 graphs as training graphs, while using the remaining ones for testing:</p></div>
 
 <pre class='language-julia'><code class='language-julia'>train_data, test_data = splitobs((graphs, y), at = 150, shuffle = true) |&gt; getobs</code></pre>
-<pre class="code-output documenter-example-output" id="var-train_data">((GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}[GNNGraph(12, 24) with x: 7×12 data, GNNGraph(22, 50) with x: 7×22 data, GNNGraph(23, 54) with x: 7×23 data, GNNGraph(25, 56) with x: 7×25 data, GNNGraph(16, 36) with x: 7×16 data, GNNGraph(11, 22) with x: 7×11 data, GNNGraph(18, 38) with x: 7×18 data, GNNGraph(23, 52) with x: 7×23 data, GNNGraph(22, 50) with x: 7×22 data, GNNGraph(20, 46) with x: 7×20 data  …  GNNGraph(16, 34) with x: 7×16 data, GNNGraph(13, 28) with x: 7×13 data, GNNGraph(21, 44) with x: 7×21 data, GNNGraph(17, 38) with x: 7×17 data, GNNGraph(23, 54) with x: 7×23 data, GNNGraph(12, 24) with x: 7×12 data, GNNGraph(22, 50) with x: 7×22 data, GNNGraph(19, 42) with x: 7×19 data, GNNGraph(16, 34) with x: 7×16 data, GNNGraph(16, 36) with x: 7×16 data], Bool[1 0 … 1 0; 0 1 … 0 1]), (GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}[GNNGraph(21, 44) with x: 7×21 data, GNNGraph(22, 50) with x: 7×22 data, GNNGraph(16, 34) with x: 7×16 data, GNNGraph(27, 66) with x: 7×27 data, GNNGraph(13, 26) with x: 7×13 data, GNNGraph(20, 44) with x: 7×20 data, GNNGraph(19, 44) with x: 7×19 data, GNNGraph(20, 46) with x: 7×20 data, GNNGraph(16, 34) with x: 7×16 data, GNNGraph(13, 28) with x: 7×13 data  …  GNNGraph(11, 22) with x: 7×11 data, GNNGraph(20, 46) with x: 7×20 data, GNNGraph(16, 34) with x: 7×16 data, GNNGraph(18, 40) with x: 7×18 data, GNNGraph(13, 28) with x: 7×13 data, GNNGraph(20, 44) with x: 7×20 data, GNNGraph(14, 30) with x: 7×14 data, GNNGraph(13, 26) with x: 7×13 data, GNNGraph(21, 44) with x: 7×21 data, GNNGraph(22, 50) with x: 7×22 data], Bool[0 0 … 0 0; 1 1 … 1 1]))</pre>
+<pre class="code-output documenter-example-output" id="var-train_data">((GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}[GNNGraph(16, 34) with x: 7×16 data, GNNGraph(22, 50) with x: 7×22 data, GNNGraph(23, 54) with x: 7×23 data, GNNGraph(11, 22) with x: 7×11 data, GNNGraph(17, 38) with x: 7×17 data, GNNGraph(13, 28) with x: 7×13 data, GNNGraph(19, 44) with x: 7×19 data, GNNGraph(16, 34) with x: 7×16 data, GNNGraph(14, 30) with x: 7×14 data, GNNGraph(18, 38) with x: 7×18 data  …  GNNGraph(12, 26) with x: 7×12 data, GNNGraph(19, 40) with x: 7×19 data, GNNGraph(19, 44) with x: 7×19 data, GNNGraph(26, 60) with x: 7×26 data, GNNGraph(20, 44) with x: 7×20 data, GNNGraph(20, 44) with x: 7×20 data, GNNGraph(17, 38) with x: 7×17 data, GNNGraph(19, 44) with x: 7×19 data, GNNGraph(19, 42) with x: 7×19 data, GNNGraph(22, 50) with x: 7×22 data], Bool[0 0 … 0 0; 1 1 … 1 1]), (GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}[GNNGraph(26, 60) with x: 7×26 data, GNNGraph(15, 34) with x: 7×15 data, GNNGraph(11, 22) with x: 7×11 data, GNNGraph(24, 50) with x: 7×24 data, GNNGraph(17, 38) with x: 7×17 data, GNNGraph(21, 44) with x: 7×21 data, GNNGraph(17, 38) with x: 7×17 data, GNNGraph(13, 28) with x: 7×13 data, GNNGraph(12, 26) with x: 7×12 data, GNNGraph(17, 38) with x: 7×17 data  …  GNNGraph(12, 26) with x: 7×12 data, GNNGraph(23, 52) with x: 7×23 data, GNNGraph(12, 24) with x: 7×12 data, GNNGraph(23, 50) with x: 7×23 data, GNNGraph(13, 28) with x: 7×13 data, GNNGraph(18, 40) with x: 7×18 data, GNNGraph(16, 36) with x: 7×16 data, GNNGraph(13, 26) with x: 7×13 data, GNNGraph(28, 62) with x: 7×28 data, GNNGraph(11, 22) with x: 7×11 data], Bool[0 0 … 0 1; 1 1 … 1 0]))</pre>
 
 <pre class='language-julia'><code class='language-julia'>begin
     train_loader = DataLoader(train_data, batchsize = 32, shuffle = true)
@@ -113,7 +113,7 @@ end</code></pre>
   (32-element Vector{GraphNeuralNetworks.GNNGraphs.GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}}, 2×32 OneHotMatrix(::Vector{UInt32}) with eltype Bool,)</pre>
 
 
-<div class="markdown"><p>Here, we opt for a <code>batch_size</code> of 32, leading to 5 (randomly shuffled) mini-batches, containing all <span class="tex">$4 \cdot 32+22 = 150$</span> graphs.</p></div>
+<div class="markdown"><p>Here, we opt for a <code>batch_size</code> of 32, leading to 5 (randomly shuffled) mini-batches, containing all <span class="tex">\(4 \cdot 32+22 = 150\)</span> graphs.</p></div>
 
 
 ```
@@ -123,15 +123,15 @@ end</code></pre>
 <p>Since graphs in graph classification datasets are usually small, a good idea is to <strong>batch the graphs</strong> before inputting them into a Graph Neural Network to guarantee full GPU utilization. In the image or language domain, this procedure is typically achieved by <strong>rescaling</strong> or <strong>padding</strong> each example into a set of equally-sized shapes, and examples are then grouped in an additional dimension. The length of this dimension is then equal to the number of examples grouped in a mini-batch and is typically referred to as the <code>batchsize</code>.</p><p>However, for GNNs the two approaches described above are either not feasible or may result in a lot of unnecessary memory consumption. Therefore, GraphNeuralNetworks.jl opts for another approach to achieve parallelization across a number of examples. Here, adjacency matrices are stacked in a diagonal fashion (creating a giant graph that holds multiple isolated subgraphs), and node and target features are simply concatenated in the node dimension (the last dimension).</p><p>This procedure has some crucial advantages over other batching procedures:</p><ol><li><p>GNN operators that rely on a message passing scheme do not need to be modified since messages are not exchanged between two nodes that belong to different graphs.</p></li><li><p>There is no computational or memory overhead since adjacency matrices are saved in a sparse fashion holding only non-zero entries, <em>i.e.</em>, the edges.</p></li></ol><p>GraphNeuralNetworks.jl can <strong>batch multiple graphs into a single giant graph</strong>:</p></div>
 
 <pre class='language-julia'><code class='language-julia'>vec_gs, _ = first(train_loader)</code></pre>
-<pre class="code-output documenter-example-output" id="var-vec_gs">(GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}[GNNGraph(17, 38) with x: 7×17 data, GNNGraph(19, 42) with x: 7×19 data, GNNGraph(13, 28) with x: 7×13 data, GNNGraph(14, 30) with x: 7×14 data, GNNGraph(13, 28) with x: 7×13 data, GNNGraph(23, 54) with x: 7×23 data, GNNGraph(16, 36) with x: 7×16 data, GNNGraph(24, 50) with x: 7×24 data, GNNGraph(23, 54) with x: 7×23 data, GNNGraph(15, 34) with x: 7×15 data  …  GNNGraph(16, 34) with x: 7×16 data, GNNGraph(16, 34) with x: 7×16 data, GNNGraph(23, 54) with x: 7×23 data, GNNGraph(12, 26) with x: 7×12 data, GNNGraph(17, 38) with x: 7×17 data, GNNGraph(20, 44) with x: 7×20 data, GNNGraph(13, 28) with x: 7×13 data, GNNGraph(26, 60) with x: 7×26 data, GNNGraph(23, 54) with x: 7×23 data, GNNGraph(24, 50) with x: 7×24 data], Bool[0 0 … 0 0; 1 1 … 1 1])</pre>
+<pre class="code-output documenter-example-output" id="var-vec_gs">(GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}[GNNGraph(19, 44) with x: 7×19 data, GNNGraph(20, 46) with x: 7×20 data, GNNGraph(15, 34) with x: 7×15 data, GNNGraph(25, 56) with x: 7×25 data, GNNGraph(17, 38) with x: 7×17 data, GNNGraph(20, 44) with x: 7×20 data, GNNGraph(16, 34) with x: 7×16 data, GNNGraph(11, 22) with x: 7×11 data, GNNGraph(19, 44) with x: 7×19 data, GNNGraph(20, 44) with x: 7×20 data  …  GNNGraph(12, 24) with x: 7×12 data, GNNGraph(12, 26) with x: 7×12 data, GNNGraph(16, 36) with x: 7×16 data, GNNGraph(11, 22) with x: 7×11 data, GNNGraph(22, 50) with x: 7×22 data, GNNGraph(13, 28) with x: 7×13 data, GNNGraph(14, 30) with x: 7×14 data, GNNGraph(16, 34) with x: 7×16 data, GNNGraph(22, 50) with x: 7×22 data, GNNGraph(23, 54) with x: 7×23 data], Bool[0 0 … 0 0; 1 1 … 1 1])</pre>
 
 <pre class='language-julia'><code class='language-julia'>MLUtils.batch(vec_gs)</code></pre>
 <pre class="code-output documenter-example-output" id="var-hash102363">GNNGraph:
-  num_nodes: 585
-  num_edges: 1292
+  num_nodes: 575
+  num_edges: 1276
   num_graphs: 32
   ndata:
-	x = 7×585 Matrix{Float32}</pre>
+	x = 7×575 Matrix{Float32}</pre>
 
 
 <div class="markdown"><p>Each batched graph object is equipped with a <strong><code>graph_indicator</code> vector</strong>, which maps each node to its respective graph in the batch:</p><p class="tex">$$\textrm{graph\_indicator} = [1, \ldots, 1, 2, \ldots, 2, 3, \ldots ]$$</p></div>
@@ -154,7 +154,7 @@ end</code></pre>
 <pre class="code-output documenter-example-output" id="var-create_model">create_model (generic function with 1 method)</pre>
 
 
-<div class="markdown"><p>Here, we again make use of the <code>GCNConv</code> with <span class="tex">$\mathrm{ReLU}(x) = \max(x, 0)$</span> activation for obtaining localized node embeddings, before we apply our final classifier on top of a graph readout layer.</p><p>Let's train our network for a few epochs to see how well it performs on the training as well as test set:</p></div>
+<div class="markdown"><p>Here, we again make use of the <code>GCNConv</code> with <span class="tex">\(\mathrm{ReLU}(x) = \max(x, 0)\)</span> activation for obtaining localized node embeddings, before we apply our final classifier on top of a graph readout layer.</p><p>Let's train our network for a few epochs to see how well it performs on the training as well as test set:</p></div>
 
 <pre class='language-julia'><code class='language-julia'>function eval_loss_accuracy(model, data_loader, device)
     loss = 0.0