Graph Neural Networks (GNN) are deep learning models that are well adapted to data in the form of graphs with feature vectors associated with nodes and edges. GNNs are a growing area of research and find many applications in complex network analysis, relational reasoning, combinatorial optimization, molecule generation, and many other fields.
GraphNeuralNetworks.jl is a pure Julia package for GNNs equipped with many features. It implements common graph convolutional layers, with CUDA support and graph batching for fast parallel operations. There are several ways by which the package could be improved.
As part of the documentation and for bootstrapping new projects, we want to add fully worked-out examples and applications of graph neural networks. We can start with entry-level tutorials and progressively introduce the reader to more advanced features.
Duration: 175h.
Expected difficulty: easy.
Expected outcome: A few pedagogical and more advanced examples of graph neural network applications.
Provide Julia-friendly wrappers for common graph datasets in MLDatasets.jl. Create convenient interfaces for the Julia ML and data ecosystem.
Duration: 175h.
Expected difficulty: easy.
Expected outcome: A large collection of graph datasets easily available to the Julia ecosystem.
Many graph convolutional layers can be expressed as non-materializing algebraic operations involving the adjacency matrix instead of the slower and more memory-consuming gather/scatter mechanism. We aim at extending as far as possible and in a gpu-friendly way these fused implementation. The project will involve fixing some outstanding issues in CUDA.jl that are blocking sparse adjacency matrix support on GPU.
Duration: 350h.
Expected difficulty: hard.
Expected outcome: A noticeable performance increase for many graph convolutional operations.
We currently support scatter/gather operation only on CPU and CUDA hardware. We aim to extend this to AMDGPU and Apple Silicon leveraging KernelAbstractions.jl, AMDGPU.jl, and Metal.jl.
Duration: 175h.
Expected difficulty: medium.
Expected outcome: Graph convolution speedup for AMD GPU and Apple hardware, performance roughly on par with CUDA.
Carlo Lucibello (author of GraphNeuralNetworks.jl). Feel free to contact me on the Julia Slack Workspace or by opening an issue in the GitHub repo.