DHG Overview

DHG (DeepHypergraph) is a deep learning library built upon PyTorch for learning with both Graph Neural Networks and Hypergraph Neural Networks. It is a general framework that supports both low-order and high-order message passing like from vertex to vertex, from vertex in one domain to vertex in another domain, from vertex to hyperedge, from hyperedge to vertex, from vertex set to vertex set.

It supports a wide variety of structures like low-order structures (graph, directed graph, bipartite graph, etc.), high-order structures (hypergraph, etc.). Various spectral-based operations (like Laplacian-based smoothing) and spatial-based operations (like message psssing from domain to domain) are integrated inside different structures. It provides multiple common metrics for performance evaluation on different tasks. Many state-of-the-art models are implemented and can be easily used for research. We also provide various visualization tools for both low-order structures and high-order structures.

In addition, DHG’s dhg.experiments module (that implements Auto-ML upon Optuna) can help you automatically tune the hyper-parameters of your models in training and easily outperforms the state-of-the-art models.

News

  • 2024-01-31 -> The v0.9.4 is now available! Fix some bugs and more datasets are included!

  • 2022-12-28 -> The v0.9.3 is now available! More datasets and operations of hypergraph are included!

  • 2022-09-25 -> The v0.9.2 is now available! More datasets, SOTA models, and visualizations are included!

  • 2022-08-25 -> The v0.9.1 is now available!

Citing

If you find our library useful for your research, please cite our papers:

@article{gao2022hgnn,
   title={HGNN $\^{}+ $: General Hypergraph Neural Networks},
   author={Gao, Yue and Feng, Yifan and Ji, Shuyi and Ji, Rongrong},
   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
   year={2022},
   publisher={IEEE}
}

@inproceedings{feng2019hypergraph,
   title={Hypergraph neural networks},
   author={Feng, Yifan and You, Haoxuan and Zhang, Zizhao and Ji, Rongrong and Gao, Yue},
   booktitle={Proceedings of the AAAI conference on artificial intelligence},
   volume={33},
   number={01},
   pages={3558--3565},
   year={2019}
}