CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs

This repository contains the code for the LoG 2024 paper "CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs". The code is based on https://github.com/BorgwardtLab/TOGL and https://github.com/ExpectationMax/torch_persistent_homology

Warning: we are in the process of clearning the repository, please forgive us if the current code is a bit messy

Installation

conda install python==3.10
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
conda install lightning -c conda-forge or pip install lightning
conda install pytorch-scatter pytorch-sparse pyg -c pyg
conda install -c dglteam/label/cu117 dgl
conda install -c conda-forge gudhi
conda install -c conda-forge graph-tool

cd repos/torch_persistent_homology/torch_persistent_homology
python setup.py install

Training models

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

$ python topognn/train_model.py --model TopoGNN --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] [--depth DEPTH]
                      [--lr LR] [--lr_patience LR_PATIENCE] [--min_lr MIN_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
  --depth DEPTH
  --lr LR
  --lr_patience LR_PATIENCE
  --min_lr MIN_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

Citation

If you use this code, please cite our paper.

@inproceedings{buffelli2024CliquePH,
    title={CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs},
    author={Davide Buffelli and Farzin Soleymani and Bastian Rieck},
    year={2024},
    booktitle={Learning on Graphs Conference (LoG)}
}