153 Stars
Updated Last
4 Months Ago
Started In
August 2021



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.


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 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:

  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.