hieubkvn123/BORF

Main code-base for "Revisiting Over-smoothing and Over-squashing via the Ollivier Ricci Curvature"

BORF

BORF: Batch Ollivier Ricci Flow for unifying and addressing over-smoothing and over-squashing in GNN.

Requirements

To configure and activate the conda environment for this repository, run

conda env create -f environment.yml
conda activate oversquashing
pip install -r requirements.txt

Experiments

1. For graph classification

To run experiments for the TUDataset benchmark, run the file run_graph_classification.py. The following command will run the benchmark for BORF with 20 iterations:

python run_graph_classification.py --rewiring brf --num_iterations 20

To add options for number of edges added and removed for rewiring, add the --brf_batch_add and --brf_batch_remove options

# Runs BORF with 3 batches, add 3 edges per batch and remove 1 edge per batch
python run_graph_classification.py --rewiring brf --num_iterations 3 \
	--brf_batch_add 3 \
	--brf_batch_remove 1

2. For node classification

To run node classification, simply change the script name to run_node_classification.py. For example:

python run_node_classification.py --rewiring brf --num_iterations 3 \
	--brf_batch_add 3 \
	--brf_batch_remove 1

Citation and reference

For technical details and full experiment results, please check our paper.

@inproceedings{
nguyen2023revisiting,
title={Revisiting Over-smoothing and Over-squashing Using {Ollivier-Ricci} Curvature},
author={Khang Nguyen and Hieu Nong and Vinh Nguyen and Nhat Ho and Stanley Osher and Tan Nguyen},
booktitle={International Conference on Machine Learning},
year={2023}
}