GlobalFeaturesGNNs

Within this repository, you will find the code belonging to the GNN section of the submission titled Extending Graph Neural Networks with Global Features (LOG, 2023).

Setup

1. Clone repository

git clone https://github.com/andreibrasoveanu97/hom-count/
cd hom-count

2. Create and activate conda environment (this assume miniconda is installed)

conda create --name HOM
conda activate HOM

3. 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

4. Install PyTorch (Geometric)

conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 -c pytorch
conda install -c pyg pyg=2.2.0
conda install openbabel fsspec rdkit graph-tool -c conda-forge

5. Install remaining dependencies

pip install -r requirements.txt

Recreating experiments

Run experimentes with the following scripts. Results will be in the Results directory.

Main experiments. Global features against no feature attached:

bash Scripts/GlobalFeatures_Individual/ZINC.sh
bash Scripts/GlobalFeatures_Individual/ogbg-molhiv.sh

Ablation. Impact of random noise in global features:

python Exp/run_experiment.py -grid "Configs/Eval_ZINC/gin_with_features_individual.yaml" -dataset "ZINC" --candidates 1  --repeats 10 --graph_feat "Counts/GlobalFeatures/ZINC_DUMMY_global.json"
python Exp/run_experiment.py -grid "Configs/Eval/gin_with_features_individual.yaml" -dataset "ogbg-molhiv" --candidates 1  --repeats 10 --graph_feat "Counts/GlobalFeatures/OGBG-MOLHIV_DUMMY_global.json"