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Equivariant Hypergraph Neural Network (PyTorch)

Equivariant Hypergraph Neural Networks
Jinwoo Kim, Saeyoon Oh, Sungjun Cho, Seunghoon Hong
ECCV 2022

image-ehnn

Setting up experiments

For hypergraph matching, please follow the instructions in hypergraph-matching/README.md.

For all other experiments, please choose and follow one of the procedures below.

Using the provided Docker image (recommended)

docker pull jw9730/ehnn:latest
docker run -it --gpus=all --ipc=host --name=ehnn -v /home:/home jw9730/ehnn:latest bash
# upon completion, you should be at /ehnn inside the container

Using the provided Dockerfile

git clone https://github.com/jw9730/ehnn.git /ehnn
cd ehnn
docker build --no-cache --tag ehnn:latest .
docker run -it --gpus all --ipc=host --name=ehnn -v /home:/home ehnn:latest bash
# upon completion, you should be at /ehnn inside the container

Using pip

sudo apt-get update
sudo apt-get install python3.9
git clone https://github.com/jw9730/ehnn.git ehnn
cd ehnn
bash install.sh

Running experiments

Runtime and memory analysis

cd runtime-and-memory-analysis

bash run_tests.sh

k-edge identification

cd k-edge-identification

# EHNN
bash scripts/ehnn_mlp/[CONFIG].sh
bash scripts/ehnn_transformer/[CONFIG].sh

# Message-passing baselines
bash scripts/alldeepsets/[CONFIG].sh
bash scripts/allsettransformer/[CONFIG].sh

# Ablations
bash scripts/ehnn_mlp_wo_global/[CONFIG].sh
bash scripts/ehnn_mlp_wo_order/[CONFIG].sh
bash scripts/ehnn_mlp_wo_global_order/[CONFIG].sh
bash scripts/ehnn_naive/[CONFIG].sh
bash scripts/ehnn_naive_hypernetwork/[CONFIG].sh

Semi-supervised node classification

cd semi-supervised-node-classification

# Run grid search
bash scripts/grid/ehnn_mlp/[DATASET].sh
bash scripts/grid/ehnn_transformer/[DATASET].sh

# Run our best configuration found from the grid search
bash scripts/grid_best/ehnn_mlp/[DATASET].sh
bash scripts/grid_best/ehnn_transformer/[DATASET].sh

Hypergraph matching

cd hypergraph-matching

# Willow ObjectClass dataset
bash run_all_experiments_willow.sh

# PASCAL VOC dataset
bash run_all_experiments_voc.sh

References

Our implementation uses code from the following repositories:

  • AllSet for semi-supervised node classification experiment pipeline
  • ThinkMatch for hypergraph matching experiment pipeline

Citation

If you find our work useful, please consider citing it:

@article{kim2022equivariant,
  author    = {Jinwoo Kim and Saeyoon Oh and Sungjun Cho and Seunghoon Hong},
  title     = {Equivariant Hypergraph Neural Networks},
  journal   = {arXiv},
  volume    = {abs/2208.10428},
  year      = {2022},
  url       = {https://arxiv.org/abs/2208.10428}
}

Acknowledgements

The development of this open-sourced code was supported in part by the National Research Foundation of Korea (NRF) (No. 2021R1A4A3032834).