BasicGNNProject

This repository is meant as a starting point for your own GNN research projects. This code allows you to tune, train and evaluate basic models on well known graph datasets.

Projects based on this repository:

• Expressivity-Preserving GNN Simulation, NeurIPS, 2023: paper, code
• Expectation-Complete Graph Representations with Homomorphisms, ICML, 2023: paper, code
• Weisfeiler and Leman Return with Graph Transformations, MLG@ECMLPKDD, 2022: paper, code
• Reducing Learning on Cell Complexes to Graphs, GTRL@ICLR, 2022, paper, code

If you find this repository helpful please give it a ⭐.

Supported Models and Datasets

Models:

• Message Pasing Graph Neural Networks: GIN, GCN, GAT
• Equivariant Subgraph Aggregation Networks: DS, DSS
• Multilayer perceptron that ignores the graph structure: MLP

Datasets:

• ZINC
• CSL: please use cross validation for this dataset
• OGB datasets: ogbg-molhiv, ogbg-moltox21, ogbg-molesol, ogbg-molbace, ogbg-molclintox, ogbg-molbbbp, ogbg-molsider, ogbg-moltoxcast, ogbg-mollipo
• Long Range Graph Benchmark datasets: Peptides-struct, Peptides-func, PascalVOC-SP
• QM9: QM9 or QM9_i if you only want to predict the i-th property

Setup

Clone this repository and open the directory

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

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

Tracking

Per default, experiments are tracked tracked via wandb. This can be disabled in Configs/config.yaml. If you want to make use of this tracking you need a wandb account. The first time you train a model, you will be prompted to enter you wandb API key. If you want to disable tracking you can do this in the config Configs/config.yaml.

How to Train a GNN

Running a Model

To train a GNN $GNN once on a datasets $dataset run

python Exp/run_model.py --model $GNN --dataset $dataset

For example python Exp/run_model.py --model GIN --dataset ZINC. This trains the GNN GIN on the ZINC dataset a single time. The result of the training will be shown in the terminal. The different hyperparameters of the GNN can be set via commandline parameters. For more details call python Exp/run_model.py -h.

Running a Series of Experiments

The script Exp/run_experiment.py optimizes hyperparameters over a parameter grid and then evaluates the parameters with the best performance on the validation set multiple times. For example:

python Exp/run_experiment.py -grid Configs/Benchmark/GIN_grid.yaml -dataset ogbg-molesol --candidates 20 --repeats 10 

This command tries 20 hyperparameter configurations defined in the GIN_grid.yaml config on the ogbg-molesol dataset and evaluates the best parameters 10 times. The result of these experiments will be stored in the directory Results/ogbg-molesol_GIN_grid.yaml, the averages of the best parameters are stored in final.json. If you have a dataset that requires cross-validation (e.g. CSL), then you need to set the number of folds (for example --folds 10).

Tuning Hyperparameters with WandB

As Exp/run_model.py allows to set model hyperparameters from the commandline, we can use WandB sweeps to optimize hyperparameters. Here is a short guide, you need to specify your parameter and scripts to run in a config file (see Configs/WandB_grids/example_grid.yaml). The sweep can then be initialized with

wandb sweep Configs/WandB_Grids/example_grid.yaml

This command will tell you the command needed to join agents to the sweep. You can even join agents on different computers to the same sweep! Sweeps can also be initialized purely from scripts. More details on sweeps be found here.

Testing

To run integration tests

python -m unittest

Citations

Models

• GIN: How Powerful are Graph Neural Networks?; Xu et al.; ICLR 2019
• GCN: Semi-Supervised Classification with Graph Convolutional Networks; Kipf and Welling; ICLR 2017
• GAT: Graph Attention Networks; Veličković at al.; ICLR 2018
• DS and DSS: Equivariant Subgraph Aggregation Networks; Bevilacqua et al.; ICLR 2022

Datasets

• ZINC: Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules; Gómez-Bombarelli et al.; ACS Central Science 2018
• ZINC: ZINC 15 – Ligand Discovery for Everyone; Sterling and Irwin; Journal of Chemical Information and Modeling 2018
• CSL: Relational Pooling for Graph Representations; Murphy et al.; ICML 2019
• OGB: Open Graph Benchmark: Datasets for Machine Learning on Graph; Hu et al.; NeurIPS 2020
• Long Range Graph Benchmark: Long Range Graph Benchmark; Dwivedi et al.; NeurIPS 2022
• QM9: MoleculeNet: A Benchmark for Molecular Machine Learning; Wu et al.; Chemical Science 2018