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
- Create
checkpoint/
anddata/preprocessed
directories - Extract the zip file downloaded from the section above to
data/preprocessed
- E.g., extract the content of
IMDB_processed.zip
todata/preprocessed/IMDB_processed
- E.g., extract the content of
- 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}
}