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A graph neural network built with TensorFlow2.

Project description

Graph Neural Network (GNN)

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Graph Neural Network library written in TensorFlow 2 and roughly based on the work of Battaglia et al..

Supported layer types:

  • Dense layer with dropout
  • Probabilistic layer (planned)

Getting Started

First, install the virtual python environment by running bash Before executing the bash scripts, make sure that these are executable using chmod -x *.sh. You can enter the virtual environment using source

Once, the basics are set-up, run bazel test ... to make sure all is up and running.


In the example provided, the GNN learns the costs of nodes based on the sum of distances to three neighboring nodes. To run this example use this command: bazel run //examples:cost_based_on_dist.


Figure 1: Graph shows the real (c) and the predicted costs by the GNN (p).


This library is an open source project available on Github and I cordially invite everyone to contribute to this project.

Copyright (c) 2019 Patrick Hart

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