GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework Flux.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.
• Heterogeneous and temporal graphs.

GraphNeuralNetworks.jl is a registered Julia package. You can easily install it through the package manager:

pkg> add GraphNeuralNetworks

Usage examples can be found in the examples and in the notebooks folder. Also, make sure to read the documentation for a comprehensive introduction to the library.

If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate the following reference:

@misc{Lucibello2021GNN,
  author       = {Carlo Lucibello and other contributors},
  title        = {GraphNeuralNetworks.jl: a geometric deep learning library for the Julia programming language},
  year         = 2021,
  url          = {https://github.com/CarloLucibello/GraphNeuralNetworks.jl}
}

GraphNeuralNetworks.jl is largely inspired by PyTorch Geometric, Deep Graph Library, and GeometricFlux.jl.