Equivariant Hypergraph Neural Networks
Jinwoo Kim, Saeyoon Oh, Sungjun Cho, Seunghoon Hong
ECCV 2022
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 containerUsing 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 containerUsing pip
sudo apt-get update
sudo apt-get install python3.9
git clone https://github.com/jw9730/ehnn.git ehnn
cd ehnn
bash install.shRuntime and memory analysis
cd runtime-and-memory-analysis
bash run_tests.shk-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].shSemi-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].shHypergraph matching
cd hypergraph-matching
# Willow ObjectClass dataset
bash run_all_experiments_willow.sh
# PASCAL VOC dataset
bash run_all_experiments_voc.shOur implementation uses code from the following repositories:
- AllSet for semi-supervised node classification experiment pipeline
- ThinkMatch for hypergraph matching experiment pipeline
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}
}The development of this open-sourced code was supported in part by the National Research Foundation of Korea (NRF) (No. 2021R1A4A3032834).
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