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https://github.com/bayer-science-for-a-better-life/phc-gnn

Implementation of the Paper: "Parameterized Hypercomplex Graph Neural Networks for Graph Classification" by Tuan Le, Marco Bertolini, Frank Noé and Djork-Arné Clevert
https://github.com/bayer-science-for-a-better-life/phc-gnn

deep-learning graph-classification graph-neural-networks graph-representation-learning hypercomplex neural-message-passing quaternion

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Implementation of the Paper: "Parameterized Hypercomplex Graph Neural Networks for Graph Classification" by Tuan Le, Marco Bertolini, Frank Noé and Djork-Arné Clevert

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README 

        
# Parameterized Hypercomplex Graph Neural Networks (PHC-GNNs)



PHC-GNNs (Le et al., 2021): https://arxiv.org/abs/2103.16584



PHM Linear Layer Illustration | PHC-GNN Layer Computation Diagram

---  | ---

![](figures/phm_illustration.png "Illustration of the PHM-Linear Layer") | ![](figures/phc_gnn_layer.png "Illustration of the PHC-GNN Layer")



### Overview

Here we provide the implementation of Parameterized Hypercomplex Graph Neural Networks (PHC-GNNs) in [PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric),

along with 6 minimal execution examples in the `benchmarks/` directory.

  

  

This repository is organised as follows:

* `phc/hypercomplex/` contains the implementation of the PHC-GNN with all its submodules. This directory resembles the

`quaternion/` in most cases, with the user-defined phm-dimension `n`. For more details, check the subdirectory README.md

* `phc/quaternion/` contains the implementation for quaternion GNN with all its submodules.  For more details, check the subdirectory README.md

* `benchmarks/` contains the python training-scripts for 3 datasets from [Open Graph Benchmark (OGB)](https://github.com/snap-stanford/ogb) and 3 datasets from 

[Benchmarking-GNNs](https://github.com/graphdeeplearning/benchmarking-gnns). Additionally, we provide 6 bash-scripts

with default arguments to run our models.  



Generally speaking, the `phc/hypercomplex/` subdirectory also includes the quaternion-valued GNN, with the modification to only work on `torch.Tensor` objects.

The `phc/quaternion/` subdirectory was first implemented with the **fixed** rules of the quaternion-algebra, such as how to perform addition, and multiplication which can be summarized in the quaternion-valued affine transformation.

The `phc/hypercomplex/` directory generalizes such operations to work directly on `torch.Tensor` objects, making it applicable to many already existing projects.  

For completeness and to share our initial motivation of this project, we also provide the implementations from the `phc/quaternion/` subdirectory.



### Installation

#### Requirements

To run our examples, the main requirements are listed in the `environment_gpu.yml` file. The main requirements used are the following:

```

python=3.8.5

pytest=6.2.1

cudatoolkit=10.1

cudnn=7.6.5

numpy=1.19.2

scipy=1.5.2

pytorch=1.7.1

torch-geometric=1.6.1

ogb=1.2.4

```



#### Conda

Create a new environment:

```

git clone https://github.com/bayer-science-for-a-better-life/phc-gnn.git

cd phc-gnn

conda env create -f environment_gpu.yml

conda activate phc-gnn

```

Install Pytorch Geometric and this module with pip by executing the bash-script `install_pyg.sh`

```

chmod +x install_pyg.sh

bash install_pyg.sh



#install this library

pip install -e .

```



Run the implemented pytests in the subdirectories, by executing:

```

pytest .

```



### Getting started

Run our example scripts in the `benchmarks/` directory. Make sure to have the `phc-gnn` environment activated.

For more details, please have a look at `benchmarks/README.md`.



### Reference

If you make use of the implementations of quaternion or parameterized hypercomplex GNN in your research, please cite our manuscript:

```

@misc{le2021parameterized,

      title={Parameterized Hypercomplex Graph Neural Networks for Graph Classification}, 

      author={Tuan Le and Marco Bertolini and Frank Noé and Djork-Arné Clevert},

      year={2021},

      eprint={2103.16584},

      archivePrefix={arXiv},

      primaryClass={cs.LG},

      url={https://arxiv.org/abs/2103.16584}

}

```



### License

GPL-3