GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia
This talk introduces GraphNeuralNetworks.jl, a Julia-based framework for deep learning on graphs. It supports both dense and sparse graphs, multiple GPU backends, and flexible manipulation of standard, heterogeneous, and temporal structures. The framework provides gather/scatter message-passing primitives for defining custom layers, along with a collection of standard layers for rapid prototyping. Real-world use cases and ongoing developments will also be discussed.
