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.

• 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.

• Python >= 3.6

• PyTorch >= 2.3.0

• DGL >= 2.2.1

• CPU or NVIDIA GPU, Linux, Python3

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:

• install from pypi

pip install openhgnn

• install from source

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 .

5. Install gdbi(Optional):

• install gdbi from git

pip install git+https://github.com/xy-Ji/gdbi.git

• install graph database from pypi

pip install neo4j==5.16.0
pip install nebula3-python==3.4.0

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][--use_database]

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.

--use_database get dataset from database

---mini_batch_flag train model with mini-batchs

---graphbolt mini-batch training with dgl.graphbolt

---use_distributed train model with distributed way

e.g.:

python main.py -m GTN -d imdb4GTN -t node_classification -g 0 --use_best_config

python main.py -m RGCN -d imdb4GTN -t node_classification -g 0 --mini_batch_flag --graphbolt

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.

tensorboard --logdir=./openhgnn/output/{model_name}/

e.g.：

tensorboard --logdir=./openhgnn/output/RGCN/

Note: To visualize results, you need to train the model first.

take neo4j and imdb dataset for example

• construct csv file for dataset(node-level:A.csv,edge-level:A_P.csv)
• import csv file to database

LOAD CSV WITH HEADERS FROM "file:///data.csv" AS row
CREATE (:graphname_labelname {ID: row.ID, ... });

• add user information to access database in config.py file

self.graph_address = [graph_address]
self.user_name = [user_name]
self.password = [password]

• e.g.:

python main.py -m MAGNN -d imdb4MAGNN -t node_classification -g 0 --use_best_config --use_database

The link will give some basic usage.

• Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification[EMNLP 2019]
• Heterogeneous Information Network Embedding with Adversarial Disentangler[TKDE 2021]

OpenHGNN Team[GAMMA LAB], DGL Team and Peng Cheng Laboratory.

See more in CONTRIBUTING.

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}
}