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Jraph: A library for Graph Neural Networks in Jax

Project description


Jraph - A library for graph neural networks in jax.

Quick Start | Documentation

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 utilities for working with graphs, and a 'zoo' of forkable graph neural network models.


pip install jraph

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

pip install git+git://

The examples require additional dependencies. To install them please run:

pip install "jraph[examples, ogb_examples] @ git+git://"


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.

  • provides a lightweight data structure, GraphsTuple, for working with graphs.
  • provides utilities 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.
  • 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.

Quick Start

Jraph takes inspiration from the Tensorflow graph_nets library in defining a GraphsTuple data structure, which is a namedtuple that contains one or more directed graphs.

Representing Graphs - The GraphsTuple

import jraph
import jax.numpy as jnp

# Define a three node graph, each node has an integer as its feature.
node_features = jnp.array([[0.], [1.], [2.]])

# We will construct a graph for which there is a directed edge between each node
# and its successor. We define this with `senders` (source nodes) and `receivers`
# (destination nodes).
senders = jnp.array([0, 1, 2])
receivers = jnp.array([1, 2, 0])

# You can optionally add edge attributes.
edges = jnp.array([[5.], [6.], [7.]])

# We then save the number of nodes and the number of edges.
# This information is used to make running GNNs over multiple graphs
# in a GraphsTuple possible.
n_node = jnp.array([3])
n_edge = jnp.array([3])

# Optionally you can add `global` information, such as a graph label.

global_context = jnp.array([[1]]) # Same feature dimensions as nodes and edges.
graph = jraph.GraphsTuple(nodes=node_features, senders=senders, receivers=receivers,
edges=edges, n_node=n_node, n_edge=n_edge, globals=global_context)

A GraphsTuple can have more than one graph.

two_graph_graphstuple = jraph.batch([graph, graph])

The node and edge features are stacked on the leading axis.

jraph.batch([graph, graph]).nodes
>>> DeviceArray([[0.],
             [2.]], dtype=float32)

You can tell which nodes are from which graph by looking at n_node.

jraph.batch([graph, graph]).n_node
>>> DeviceArray([3, 3], dtype=int32)

You can store nests of features in nodes, edges and globals. This makes it possible to store multiple sets of features for each node, edge or graph, with potentially different types and semantically different meanings (for example 'training' and 'testing' nodes). The only requirement if that all arrays within each nest must have a common leading dimensions size, matching the total number of nodes, edges or graphs within the Graphstuple respectively.

node_targets = jnp.array([[True], [False], [True]])
graph = graph._replace(nodes={'inputs': graph.nodes, 'targets': node_targets})

Using the Model Zoo

Jraph provides a set of implemented reference models for you to use.

A Jraph model defines a message passing algorithm between the nodes, edges and global attributes of a graph. The user defines update functions that update graph features, which are typically neural networks but can be arbitrary jax functions.

Let's go through a GraphNetwork (paper) example. A GraphNet's first update function updates the edges using edge features, the node features of the sender and receiver and the global features.

# As one example, we just pass the edge features straight through.
def update_edge_fn(edge, sender, receiver, globals_):
  return edge

Often we use the concatenation of these features, and jraph provides an easy way of doing this with the concatenated_args decorator.

def update_edge_fn(concatenated_features):
  return concatenated_features

Typically, a learned model such as a Multi-Layer Perceptron is used within an update function.

The user similarly defines functions that update the nodes and globals. These are then used to configure a GraphNetwork. To see the arguments to the node and global update_fns please take a look at the model zoo.

net = jraph.GraphNetwork(update_edge_fn=update_edge_fn,

net is a function that sends messages according to the GraphNetwork algorithm and applies the update_fn. It takes a graph, and returns a graph.

updated_graph = net(graph)


For a deeper dive best place to start are the examples. In particular:

  • examples/ provides an introduction to the features of the library.
  • ogb_examples/ 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, as well as making models go fast with jax.jit, and how to deal with Jax's static shape requirement.

Citing Jraph

To cite this repository:

  author = {Jonathan Godwin* and Thomas Keck* and Peter Battaglia and Victor Bapst and Thomas Kipf and Yujia Li and Kimberly Stachenfeld and Petar Veli\v{c}kovi\'{c} and Alvaro Sanchez-Gonzalez},
  title = {{J}raph: {A} library for graph neural networks in jax.},
  url = {},
  version = {},
  year = {2020},

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