MAGNN

This repository provides a reference implementation of MAGNN as described in the paper:

MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding.
Xinyu Fu, Jiani Zhang, Ziqiao Meng, Irwin King.
The Web Conference, 2020.

Available at arXiv:2002.01680.

Dependencies

Recent versions of the following packages for Python 3 are required:

• PyTorch
• DGL
• NetworkX
• scikit-learn
• NumPy
• SciPy

Datasets

The preprocessed datasets are available at:

• IMDb - Dropbox
• DBLP - Dropbox
• Last.fm - Dropbox

Usage

1. Create checkpoint/ and data/preprocessed directories
2. Extract the zip file downloaded from the section above to data/preprocessed
   • E.g., extract the content of IMDB_processed.zip to data/preprocessed/IMDB_processed
3. Execute one of the following three commands from the project home directory:
   • python run_IMDB.py
   • python run_DBLP.py
   • python run_LastFM.py

For more information about the available options of the model, you may check by executing python run_IMDB.py --help

Citing

If you find MAGNN useful in your research, please cite the following paper:

@inproceedings{fu2020magnn,
 title={MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding},
 author={Xinyu Fu and Jiani Zhang and Ziqiao Meng and Irwin King},
 booktitle = {WWW},
 year={2020}
}