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Build Graph Nets in Tensorflow
| Date | Stars |
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| 2026-07-24 | 5403 |
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 # Graph Nets library [Graph Nets](https://github.com/deepmind/graph_nets) is DeepMind's library for building graph networks in Tensorflow and Sonnet. Contact [email protected] for comments and questions. #### What are graph networks? A graph network takes a graph as input and returns a graph as output. The input graph has edge- (*E* ), node- (*V* ), and global-level (**u**) attributes. The output graph has the same structure, but updated attributes. Graph networks are part of the broader family of "graph neural networks" (Scarselli et al., 2009). To learn more about graph networks, see our arXiv paper: [Relational inductive biases, deep learning, and graph networks](https://arxiv.org/abs/1806.01261).  ## Installation The Graph Nets library can be installed from pip. This installation is compatible with Linux/Mac OS X, and Python 2.7 and 3.4+. The library will work with both the CPU and GPU version of TensorFlow, but to allow for that it does not list Tensorflow as a requirement, so you need to install Tensorflow separately if you haven't already done so. To install the Graph Nets library and use it with TensorFlow 1 and Sonnet 1, run: (CPU) ```shell $ pip install graph_nets "tensorflow>=1.15,<2" "dm-sonnet<2" "tensorflow_probability<0.9" ``` (GPU) ```shell $ pip install graph_nets "tensorflow_gpu>=1.15,<2" "dm-sonnet<2" "tensorflow_probability<0.9" ``` To install the Graph Nets library and use it with TensorFlow 2 and Sonnet 2, run: (CPU) ```shell $ pip install graph_nets "tensorflow>=2.1.0-rc1" "dm-sonnet>=2.0.0b0" tensorflow_probability ``` (GPU) ```shell $ pip install graph_nets "tensorflow_gpu>=2.1.0-rc1" "dm-sonnet>=2.0.0b0" tensorflow_probability ``` The latest version of the library requires TensorFlow >=1.15. For compatibility with earlier versions of TensorFlow, please install v1.0.4 of the Graph Nets library. ## Usage example The following code constructs a simple graph net module and connects it to data. ```python import graph_nets as gn import sonnet as snt # Provide your own functions to generate graph-structured data. input_graphs = get_graphs() # Create the 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])) # Pass the input graphs to the graph network, and return the output graphs. output_graphs = graph_net_module(input_graphs) ``` ## Demo Jupyter notebooks The library includes demos which show how to create, manipulate, and train graph networks to reason about graph-structured data, on a shortest path-finding task, a sorting task, and a physical prediction task. Each demo uses the same graph network architecture, which highlights the flexibility of the approach. ### Try the demos in your browser in [Colaboratory](https://colab.research.google.com) To try out the demos without installing anything locally, you can run the demos in your browser (even on your phone) via a cloud Colaboratory backend. Click a demo link below, and follow the instructions in the notebook. ---------------- #### [Run "shortest path demo" in browser](https://colab.research.google.com/github/deepmind/graph_nets/blob/master/graph_nets/demos/shortest_path.ipynb) The "shortest path demo" creates random graphs, and trains a graph network to label the nodes and edges on the shortest path between any two nodes. Over a sequence of message-passing steps (as depicted by each step's plot), the model refines its prediction of the shortest path.  ---------------- #### [Run "sort demo" in browser](https://colab.research.google.com/github/deepmind
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Alvaro · DeepMind · United Kingdom
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Ram Rachum · University of California, Berkeley · Israel
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Fabrizio Milo
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Katsunori Nakanishi
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Louise Deason · @Fundamental-Technologies · United Kingdom
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Matthew Wilson · United States
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Anirudh Dagar · @aws @autogluon @d2l-ai · Germany
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Amogh Mannekote · Amazon
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:56b41b9c8e591e45, topic:deep-learning, topic:tensorflow