This repository contains the code of the GNN part of the paper Expectation-Complete Graph Representations with Homomorphisms (ICML, 2023).

1. Clone repository

git clone https://github.com/ocatias/HomCountGNNs/
cd HomCountGNNs

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

5. Install remaining dependencies

pip install -r requirements.txt

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

Main experiments. Homomorphism counts against no homomorphism counts:

bash Scripts/large_datasets.sh
bash Scripts/small_datasets.sh

Ablation. Impact of misaligned homomorphism counts:

bash Scripts/misaligned_feats.sh