Topological Graph Neural Networks

This repository contains the code for our ICLR 2022 paper “Topological Graph Neural Networks.“ This repository requires either Python 3.7 or 3.8 to be installed.

Please note: This repository is a work in progress!

Citation

If you use this code, please consider citing our paper:

@InProceedings{Horn22a,
  author        = {Horn, Max and {De Brouwer}, Edward and Moor, Michael and Moreau, Yves and Rieck, Bastian and Borgwardt, Karsten},
  title         = {Topological Graph Neural Networks},
  year          = {2022},
  booktitle     = {International Conference on Learning Representations~(ICLR)},
  url           = {https://openreview.net/pdf?id=oxxUMeFwEHd},
}

Quickstart

# Install poetry
curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -
# Create venv and install dependencies of project
poetry install
# In case any errors occur, please rerun `poetry install`
poetry run install_deps_cpu  # or of course install_deps_cuXXX for GPU support
# Train TopoGNN on DD for 10 epochs
poetry run python topognn/train_model.py --model TopoGNN --dataset DD --max_epochs 10

Installation

This repository uses poetry in order to manage dependencies and provide a virtual environment for the project. In order to install poetry please refer to the instructions here. After poetry is installed it can be used to create a virtual environment and install the dependencies of the repository:

$ poetry install
# If any errors occur please rerun the command `poetry install`

Due to some incompatibilities between poetry and torch_geometric (which was used for the implementation of GNNs in this repository), additional dependencies need to be installed in a separate step dependent on the necessity of CUDA based GPU acceleration. This can be done using the command below

$ poetry run install_deps_{cpu, cu101, cu102, cu110}

where {cpu, cu101, cu102, cu110} should be replaced with either cpu or the string matching the installed cuda toolkit version.

Training models

The repository implements two models TopoGNN and GNN. Additional parameters can be passed to the script depending on the model and dataset selected. For example, the GNN model and the MNIST dataset have the following configuration options:

$ poetry run topognn/train_model.py --model GNN --dataset MNIST --help
usage: train_model.py [-h] [--model {TopoGNN,GCN}]
                      [--dataset {IMDB-BINARY,REDDIT-BINARY,REDDIT-5K,PROTEINS,PROTEINS_full,ENZYMES,DD,MUTAG,MNIST,CIFAR10,PATTERN,CLUSTER,Necklaces,Cycles,NoCycles}]
                      [--training_seed TRAINING_SEED]
                      [--max_epochs MAX_EPOCHS] [--paired PAIRED]
                      [--merged MERGED] [--logger {wandb,tensorboard}]
                      [--gpu GPU] [--hidden_dim HIDDEN_DIM]
                      [--lr LR]
                      [--dropout_p DROPOUT_P] [--GIN GIN]
                      [--train_eps TRAIN_EPS] [--batch_norm BATCH_NORM]
                      [--residual RESIDUAL] [--batch_size BATCH_SIZE]
                      [--use_node_attributes USE_NODE_ATTRIBUTES]

optional arguments:
  -h, --help            show this help message and exit
  --model {TopoGNN,GCN}
  --dataset {IMDB-BINARY,REDDIT-BINARY,REDDIT-5K,PROTEINS,PROTEINS_full,ENZYMES,DD,MUTAG,MNIST,CIFAR10,PATTERN,CLUSTER,Necklaces,Cycles,NoCycles}
  --training_seed TRAINING_SEED
  --max_epochs MAX_EPOCHS
  --paired PAIRED
  --merged MERGED
  --logger {wandb,tensorboard}
  --gpu GPU
  --hidden_dim HIDDEN_DIM
  --lr LR
  --dropout_p DROPOUT_P
  --GIN GIN
  --train_eps TRAIN_EPS
  --batch_norm BATCH_NORM
  --residual RESIDUAL
  --batch_size BATCH_SIZE
  --use_node_attributes USE_NODE_ATTRIBUTES

Logging

By default runs are logged using Tensorboard, yet logging via WandB is also possible. For this some adaptations to the code might be necessary in order to log to the correct entity/project. The logs of Tensorboard are by default stored under the path logs/{MODEL}_{DATASET}.

TopoGNN variants and ablations

In our work we show multiple variants and ablations of the TOGL layer. In particular we present the simple variant, which does not actually perform any topological computation, but merely includes all other components of the layer. This setting can be triggered using the additional parameter --fake True. Further, we differentiate between TOGL with coordinatization functions with the parameter --deepset False and TOGL using a deep sets --deepset True. Below you can see some configurations which reflect the scenarios covered in our work.

# TopoGNN with coord functions
poetry run topognn/train_model.py --model TopoGNN --dataset DD --batch_size 20 --lr 0.0007

# GCN on DD
poetry run topognn/train_model.py --model GCN --dataset DD --batch_size 20 --lr 0.0007

Additional calls are also possible; check out available models in models.py.

Synthetic datasets

The synthetic datasets used to train our models are provided in the folder data/SYNTHETIC. If these are not compatible with your architecture (which could happen as they are saved in a binary file format), you can regenerate the synthetic datasets using calls to the script data/SYNTHETIC/datagen.py:

cd data/SYNTHETIC
poetry run python datagen.py --dataset Cycle --min_cycle 3
poetry run python datagen.py --dataset Necklaces

Results used for plots

The results and data used to plot our figures can be found in the folder plots_data and code to generate plots can be found in the notebooks folder.