Jraph (pronounced giraffe) is a lightweight library for working with graph neural networks in jax. It provides a data structure for graphs, a set of utilites for working with graphs, and a 'zoo' of forkable graph neural network models.

Jraph can be installed directly from github using the following command:

pip install git+git://github.com/deepmind/jraph.git

The best place to start are the examples. In particular:

• examples/basic.py provides an introduction to the features of the library.
• ogb_examples/train.py provides an end to end example of training a GraphNet on molhiv Open Graph Benchmark dataset. Please note, you need to have downloaded the dataset to run this example.

The rest of the examples are short scripts demonstrating how to use various models from our model zoo.

Jraph is designed to provide utilities for working with graphs in jax, but doesn't prescribe a way to write or develop graph neural networks.

• graph.py provides a lightweight data structure, GraphsTuple, for working with graphs.
• utils.py provides utilies for working with GraphsTuples in jax.
  • Utilities for batching datasets of GraphsTuples.
  • Utilities to support jit compilation of variable shaped graphs via padding and masking.
  • Utilities for defining losses on partitions of inputs.
• models.py provides examples of different types of graph neural network message passing. These are designed to be lightweight, easy to fork and adapt. They do not manage parameters for you - for that, consider using haiku or flax. See the examples for more details.