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.

• We introduce a novel edge representation learning scheme using Dual Hypergraph Transformation, which exploits the dual hypergraph whose nodes are edges of the original graph, on which we can apply off-the-shelf message-passing schemes designed for node-level representation learning.
• We propose novel edge pooling methods for graph-level representation learning, namely HyperCluster and HyperDrop, to overcome the limitations of existing node-based pooling methods.
• We validate our methods on graph reconstruction, generation, and classification tasks, on which they largely outperform existing graph representation learning methods.

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

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.

• Edge Reconstruction on the ZINC dataset

sh ./scripts/reconstruction_ZINC.sh 0 000

• Graph Classification on TU datasets

sh ./scripts/classification_TU.sh 0 000

• Graph Classification on OGB datasets

sh ./scripts/classification_OGB.sh 0 000

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}
}