Graph Nets Library
The Graph Nets library, developed by DeepMind, is a powerful tool for building graph networks using TensorFlow and Sonnet. This library provides a flexible framework for constructing and using graph-based models, which are particularly useful in tasks that involve relational data or complex interactions.
What Are Graph Networks?
Graph networks are a type of neural network that work with graph-structured data. They take a graph as input and produce another graph as output. Each graph comprises nodes, edges, and global-level attributes. The nodes and edges have their respective features, and graph networks update these features through a series of transformations, resulting in an output graph with the same structure but different attributes. Graph networks belong to the broader family known as graph neural networks.
For an in-depth understanding of graph networks, one can refer to DeepMind's paper titled "Relational Inductive Biases, Deep Learning, and Graph Networks" available on arXiv.
Installation
Installing the Graph Nets library is straightforward with pip, compatible with Linux/Mac OS X, and Python versions 2.7 and above. The library can be used with both CPU and GPU versions of TensorFlow, although TensorFlow needs to be installed separately.
For TensorFlow 1 and Sonnet 1, use the following command:
- CPU:
pip install graph_nets "tensorflow>=1.15,<2" "dm-sonnet<2" "tensorflow_probability<0.9" - GPU:
pip install graph_nets "tensorflow_gpu>=1.15,<2" "dm-sonnet<2" "tensorflow_probability<0.9"
For TensorFlow 2 and Sonnet 2, the commands are:
- CPU:
pip install graph_nets "tensorflow>=2.1.0-rc1" "dm-sonnet>=2.0.0b0" tensorflow_probability - GPU:
pip install graph_nets "tensorflow_gpu>=2.1.0-rc1" "dm-sonnet>=2.0.0b0" tensorflow_probability
Usage Example
Here's a simple example of creating a graph net module and connecting it to data:
import graph_nets as gn
import sonnet as snt
# Imagine you have a function to generate graph-structured data.
input_graphs = get_graphs()
# Create a graph network.
graph_net_module = gn.modules.GraphNetwork(
edge_model_fn=lambda: snt.nets.MLP([32, 32]),
node_model_fn=lambda: snt.nets.MLP([32, 32]),
global_model_fn=lambda: snt.nets.MLP([32, 32]))
# Process the input graphs through the network to obtain output graphs.
output_graphs = graph_net_module(input_graphs)
Demo Jupyter Notebooks
The library offers a collection of demos that illustrate how to create, manipulate, and train graph networks for various tasks, such as finding the shortest path, sorting, and predicting physical states. Each demo uses the same basic architecture, showcasing the versatility of graph networks.
Interested individuals can try these demos through a browser using Google's Colaboratory, enabling experimentation without the need for local installations. Some of the available demos include:
- Shortest Path Demo: Trains a network to recognize and label the shortest path in randomly created graphs.
- Sort Demo: Helps a graph network learn to sort a list of random numbers.
- Physics Demo: Utilizes graph networks to predict the future state of a physical system, modeled with mass-spring systems.
- Graph Nets Basics Demo: Offers a step-by-step tutorial on creating and manipulating graphs and building custom graph network modules.
Run Demos Locally
For users wishing to run demos locally, necessary dependencies can be installed using:
$ pip install jupyter matplotlib scipy
After installation, users can explore the demo notebooks by navigating to the demo directory and starting Jupyter:
$ cd <path-to-graph-nets-library>/demos
$ jupyter notebook
Other Graph Neural Network Libraries
Besides the Graph Nets library, several other high-quality, open-source libraries for graph neural networks are available:
- jraph: DeepMind's library for GNNs in JAX.
- pytorch_geometric: Features a similar interface to Graph Nets.
- Deep Graph Library (DGL): A versatile library for various graph neural network applications.
In summary, the Graph Nets library from DeepMind provides a comprehensive suite for those interested in developing graph-based deep learning models, supported by ample resources and detailed demonstrations to facilitate learning and implementation.