semvec/pypi-packages huggingface dataset:
tf-gnn 0.0.1
A graph neural network built with TensorFlow2.
Requires Python: >=3.6

# Graph Neural Network (GNN)

![CI Build](https://github.com/patrickhart/gnn/workflows/CI/badge.svg)

Graph Neural Network library written in TensorFlow 2 and roughly based on the work of [Battaglia et al.](https://arxiv.org/abs/1806.01261).

Supported layer types:

* Dense layer with dropout
* Probabilistic layer (planned)


## Getting Started

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

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


## Example

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`.

![Graph](docs/graph.png)  

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


## Contributing

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