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Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

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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 1.2.0
  • DGL 0.3.1
  • NetworkX 2.3
  • scikit-learn 0.21.3
  • NumPy 1.17.2
  • SciPy 1.3.1

Dependencies for the preprocessing code are not listed here.

Datasets

The preprocessed datasets are available at:

The GloVe word vectors are obtained from GloVe. Here is the direct link for the version we used in DBLP preprocessing.

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}
}