ICT-GIMLab / SeHGNN

Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN)

The camera-ready paper for AAAI 23 can be found at: http://arxiv.org/abs/2207.02547

Requirements

1. Neural network libraries for GNNs

• pytorch
• pytorch-geometric

Please check your cuda version first and install the above libraries matching your cuda. If possible, we recommend to install the latest versions of these libraries.

• dgl

If you want to generate ComplEx embeddings for ogbn-mag, we recommend to install dgl<1.0.

2. Other dependencies

Install other requirements:

pip install -r requirements.txt
git clone https://github.com/Yangxc13/sparse_tools.git --depth=1
cd sparse_tools
python setup.py develop
cd ..

Data preparation

• HGB datasets for node classification

These datasets include four medium-scale datasets. Please download them (DBLP.zip, ACM.zip, IMDB.zip, Freebase.zip) from HGB repository and extract content under the folder './data/'.

• Ogbn-mag

It is a large dataset from OGB challenge. Thus dataset will be automatically downloaded for the first time running.

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If you encounter any issues, please feel free to reach out to me at [e-mail]. The previous email address, [e-mail], is no longer in use.