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Edge Representation Learning with Hypergraphs

Official Code Repository for the paper "Edge Representation Learning with Hypergraphs" (NeurIPS 2021): https://arxiv.org/abs/2106.15845.

In this repository, we implement the Dual Hypergraph Transformation (DHT) and two edge pooling methods HyperDrop and HyperCluster.

<p align="center"> <img width="750" src="assets/model_overview.jpg"/> </p>

Contribution

Dependencies

EHGNN is built in Python 3.7.0 and Pytorch 1.4.0. Please use the following command to install the requirements:

pip install -r requirements.txt

additionally run the following command:

conda install -c conda-forge ogb=1.3.0
conda install -c huggingface transformers=4.4.2
conda install -c conda-forge rdkit=2020.03.3.0

Training and Evaluation

We provide the commands for the following tasks: Graph Reconstruction and Graph Classification

For each command, the first argument denotes the gpu id and the second argument denotes the experiment number.

sh ./scripts/reconstruction_ZINC.sh 0 000
sh ./scripts/classification_TU.sh 0 000
sh ./scripts/classification_OGB.sh 0 000

Citation

If you found the provided code with our paper useful in your work, we kindly request that you cite our work.

@inproceedings{jo2021ehgnn,
  author    = {Jaehyeong Jo and
               Jinheon Baek and
               Seul Lee and
               Dongki Kim and
               Minki Kang and
               Sung Ju Hwang},
  title     = {Edge Representation Learning with Hypergraphs},
  booktitle = {Advances in Neural Information Processing Systems 34: Annual Conference
               on Neural Information Processing Systems 2021, NeurIPS 2021, December
               6-14, 2021, virtual},
  pages     = {7534--7546},
  year      = {2021}
}