GraphNeuralNetworks

This is the documentation page for GraphNeuralNetworks.jl, a graph neural network library written in Julia and based on the deep learning framework Flux.jl. GraphNeuralNetworks.jl is largely inspired by PyTorch Geometric, Deep Graph Library, and GeometricFlux.jl.

Among its features:

  • Implements common graph convolutional layers.
  • Supports computations on batched graphs.
  • Easy to define custom layers.
  • CUDA support.
  • Integration with Graphs.jl.
  • Examples of node, edge, and graph level machine learning tasks.

Package overview

Let's give a brief overview of the package by solving a graph regression problem with synthetic data.

Usage examples on real datasets can be found in the examples folder.

Data preparation

We create a dataset consisting in multiple random graphs and associated data features.

using GraphNeuralNetworks, Graphs, Flux, CUDA, Statistics, MLUtils
using Flux.Data: DataLoader

all_graphs = GNNGraph[]

for _ in 1:1000
    g = GNNGraph(random_regular_graph(10, 4),  
            ndata=(; x = randn(Float32, 16,10)),  # input node features
            gdata=(; y = randn(Float32)))         # regression target   
    push!(all_graphs, g)
end

Model building

We concisely define our model as a GNNChain containing two graph convolutional layers. If CUDA is available, our model will live on the gpu.

device = CUDA.functional() ? Flux.gpu : Flux.cpu;

model = GNNChain(GCNConv(16 => 64),
                BatchNorm(64),     # Apply batch normalization on node features (nodes dimension is batch dimension)
                x -> relu.(x),     
                GCNConv(64 => 64, relu),
                GlobalPool(mean),  # aggregate node-wise features into graph-wise features
                Dense(64, 1)) |> device

ps = Flux.params(model)
opt = Adam(1f-4)

Training

Finally, we use a standard Flux training pipeline to fit our dataset. We use Flux's DataLoader to iterate over mini-batches of graphs that are glued together into a single GNNGraph using the MLUtils.batch method. This is what happens under the hood when creating a DataLoader with the collate=true option.

train_graphs, test_graphs = MLUtils.split(all_graphs, at=0.8)

train_loader = DataLoader(train_graphs, 
                batchsize=32, shuffle=true, collate=true)
test_loader = DataLoader(test_graphs, 
                batchsize=32, shuffle=false, collate=true)

loss(g::GNNGraph) = mean((vec(model(g, g.ndata.x)) - g.gdata.y).^2)

loss(loader) = mean(loss(g |> device) for g in loader)

for epoch in 1:100
    for g in train_loader
        g = g |> device
        grad = gradient(() -> loss(g), ps)
        Flux.Optimise.update!(opt, ps, grad)
    end

    @info (; epoch, train_loss=loss(train_loader), test_loss=loss(test_loader))
end