OpenHGNN
启智社区(中文版)
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch. We integrate SOTA models of heterogeneous graph.
News
We release the latest version v0.1.1 on OpenI with Chinese.
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
- OpenHGNN最新功能
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=3.7
source activate openhgnn
2. Pytorch: Install PyTorch. For example:
# CUDA versions: cpu, cu92, cu101, cu102, cu101, cu111
pip install torch==1.8.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
3. DGL: Install DGL, follow their instructions. For example:
# CUDA versions: cpu, cu101, cu102, cu110, cu111
pip install --pre dgl-cu101 -f https://data.dgl.ai/wheels-test/repo.html
4. OpenHGNN and other dependencies:
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 -r requirements.txt
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.
--use_hpo
Besides use_best_config, we give a hyper-parameter example to search the best hyper-parameter automatically.
--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.