OpenHGNN
启智社区(中文版) | **OpenHGNN [CIKM2022]** | **Space4HGNN [SIGIR2022]** | Benchmark&Leaderboard | Slack Channel
This is an open-source toolkit for Heterogeneous Graph Neural Network based
on DGL [Deep Graph Library] and PyTorch. We integrate SOTA models
of heterogeneous graph.
News
2023-02-24 OpenI Excellent Incubation Award
OpenHGNN won the Excellent Incubation Program Award of OpenI Community! For more details:https://mp.weixin.qq.com/s/PpbwEdP0-8wG9dsvRvRDaA
2023-02-21 First Prize of CIE
The algorithm library supports the project of "Intelligent Analysis Technology and Scale Application of Large Scale Complex Heterogeneous Graph Data" led by BUPT and participated by ANT GROUP, China Mobile, Haizhi Technology, etc. This project won the first prize of the 2022 Chinese Intitute of Electronics "Science and Technology Progress Award".
2023-01-13 release v0.4
We release the latest version v0.4.
- New models
- Provide pipelines for applications
- More models supporting mini-batch training
- Benchmark for million-scale graphs
2022-08-02 paper accepted
Our paper [ OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network ](https://dl.acm.org/doi/abs/10.1145/3511808.3557664) is accpeted at CIKM 2022 short paper track.
2022-06-27 release v0.3
We release the latest version v0.3.
- New models
- API Usage
- Simply customization of user-defined datasets and models
- Visualization tools of heterogeneous graphs
2022-02-28 release v0.2
We release the latest version v0.2.
2022-01-07 加入启智社区
启智社区用户可以享受到如下功能:
- 全新的中文文档
- 免费的计算资源—— 云脑使用教程
- OpenHGNN最新功能
- 新增模型:【KDD2017】Metapath2vec、【TKDE2018】HERec、【KDD2021】HeCo、【KDD2021】SimpleHGN、【TKDE2021】HPN、【ICDM2021】HDE、fastGTN
- 新增日志功能
- 新增美团外卖数据集
Key Features
- Easy-to-Use: OpenHGNN provides easy-to-use interfaces for running experiments with the given models and dataset.
Besides, we also integrate optuna to get hyperparameter optimization.
- Extensibility: User can define customized task/model/dataset to apply new models to new scenarios.
- Efficiency: The backend dgl provides efficient APIs.
Get Started
Requirements and Installation
1. Python environment (Optional): We recommend using Conda package manager
conda create -n openhgnn python=3.6
source activate openhgnn
2. Install Pytorch: Follow their tutorial to run the proper command according to
your OS and CUDA version. For example:
pip install torch torchvision torchaudio
3. Install DGL: Follow their tutorial to run the proper command according to
your OS and CUDA version. For example:
pip install dgl -f https://data.dgl.ai/wheels/repo.html
4. Install openhgnn:
pip install openhgnn
git clone https://github.com/BUPT-GAMMA/OpenHGNN
# If you encounter a network error, try git clone from openi as following.
# git clone https://git.openi.org.cn/GAMMALab/OpenHGNN.git
cd OpenHGNN
pip install .
Running an existing baseline model on an existing benchmark dataset
python main.py -m model_name -d dataset_name -t task_name -g 0 --use_best_config --load_from_pretrained
usage: main.py [-h] [--model MODEL] [--task TASK] [--dataset DATASET]
[--gpu GPU] [--use_best_config]
optional arguments:
-h, --help
show this help message and exit
--model -m
name of models
--task -t
name of task
--dataset -d
name of datasets
--gpu -g
controls which gpu you will use. If you do not have gpu, set -g -1.
--use_best_config
use_best_config means you can use the best config in the dataset with the model. If you want to
set the different hyper-parameter, modify the openhgnn.config.ini manually. The best_config
will override the parameter in config.ini.
--load_from_pretrained
will load the model from a default checkpoint.
e.g.:
python main.py -m GTN -d imdb4GTN -t node_classification -g 0 --use_best_config
Note: If you are interested in some model, you can refer to the below models list.
Refer to the docs to get more basic and depth usage.
Supported Models with specific task
The link will give some basic usage.
Candidate models
Contributors
OpenHGNN Team[GAMMA LAB], DGL Team and Peng Cheng Laboratory.
See more in CONTRIBUTING.
Cite OpenHGNN
If you use OpenHGNN in a scientific publication, we would appreciate citations to the following paper:
@inproceedings{han2022openhgnn,
title={OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network},
author={Hui Han, Tianyu Zhao, Cheng Yang, Hongyi Zhang, Yaoqi Liu, Xiao Wang, Chuan Shi},
booktitle={CIKM},
year={2022}
}