GraphNeuralNetworks.jl

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.

Installation

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

pkg> add GraphNeuralNetworks

Usage

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.

Citing

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}
}

Acknowledgments

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

License

rbSparky/GraphNeuralNetworks.jl

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