Epistemic Graph Neural Network for systematic generalisation problems

Getting started

Depending on how big this project will eventually get, we offer this project as dev package that can be built in editable mode:

Create a virtual environment and install the necessary packages below

python -m venv venv
source venv/bin/activate # for linux
.\venv\Scripts\activate # for windows
pip install -e .

Also install the following packages using the link relevant for your hardware:

pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.1.0+${CUDA}.html

e.g. CUDA=cpu for a machine without a gpu. See https://pypi.org/project/torch-sparse/

To reproduce results in the paper just build and run the following command in src

python train.py experiments=`pick a dataset model config file from configs/experiments`

The config can be easily adjusted from the command line using a dot file path notation with the experiments. prefix. For example, running for 10 epochs on the rcc8 dataset amounts to:

python train.py experiments=fb_model_rcc8 experiments.epochs=10

Cite

If you find this work/code useful, please consider citing us:

@misc{khalid2024systematicreasoning,
      title={Systematic Reasoning About Relational Domains With Graph Neural Networks}, 
      author={Irtaza Khalid and Steven Schockaert},
      year={2024},
      eprint={2407.17396},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2407.17396}, 
}