Outerplanar GNNs

Code repository for our paper Maximally Expressive GNNs for Outerplanar Graphs (LoG Extended Abstract, 2023).

Setup

Clone this repository and open the directory

Add this directory to the python path. Let $PATH be the path to where this repository is stored (i.e. the result of running pwd).

export PYTHONPATH=$PYTHONPATH:$PATH

Create a conda environment (this assume miniconda is installed)

conda create --name GNNs

Activate environment

conda activate GNNs

Install dependencies

conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 -c pytorch
conda install -c pyg pyg=2.2.0
pip install -r requirements.txt

Replicating the experiments

Results can be found in the results directory.

Baselines (GIN):

python Exp/run_experiment.py -grid Configs/Eval/GIN_zinc.yaml -dataset ZINC --candidates 48 --repeats 10
python Exp/run_experiment.py -grid Configs/Eval/GIN_molhiv.yaml -dataset ogbg-molhiv --candidates 16 --repeats 10

New models (CAT+GIN):

python Exp/run_experiment.py -grid Configs/Eval/cat_molhiv.yaml -dataset ogbg-molhiv --candidates 16 --repeats 10
python Exp/run_experiment.py -grid Configs/Eval/cat_zinc.yaml -dataset ZINC --candidates 48 --repeats 10