This is the repo for our ICASSP 2024 paper: Hypergraph-MLP: Learning on Hypergraphs without Message Passing.
A quick summary of different folders:
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'baselines_hypergnn' contains the source code for our baseline hypergraph neural networks.
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'ours' contains the source code for our Hypergraph-MLP.
conda create -n "hgmlp" python=3.7
conda activate hgmlp
conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.0 -c pytorch
pip install torch-scatter==2.0.4 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install torch-sparse==0.6.0 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install torch-cluster==1.5.2 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install torch-geometric==1.6.3 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install ipdb
pip install tqdm
pip install scipy
pip install matplotlib
To generate a dataset for training using PyG or DGL, please set up the following three directories:
p2root: './data/pyg_data/hypergraph_dataset_updated/'
p2raw: './data/AllSet_all_raw_data/'
p2dgl_data: './data/dgl_data_raw/'
Next, unzip the raw data zip file into p2raw. The raw data zip file can be found in this link.
This code is based on the official code of AllSet (Paper; Github). Sincere appreciation is extended for their valuable contributions.
If you use this code, please cite our paper:
@inproceedings{tang2024hypergraph,
title={Hypergraph-MLP: Learning on Hypergraphs without Message Passing},
author={Tang, Bohan and Chen, Siheng and Dong, Xiaowen},
booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2024},
organization={IEEE}
}