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Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
| Date | Stars |
|---|---|
| 2026-07-31 | 438 |
| 2026-08-04 | 439 |
| 2026-08-05 | 439 |
| 2026-08-06 | 439 |
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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.<br>
> Xinyu Fu, Jiani Zhang, Ziqiao Meng, Irwin King.<br>
> The Web Conference, 2020.
Available at [arXiv:2002.01680](https://arxiv.org/abs/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:
* IMDb - [Dropbox](https://www.dropbox.com/s/g0btk9ctr1es39x/IMDB_processed.zip?dl=0)
* DBLP - [Dropbox](https://www.dropbox.com/s/yh4grpeks87ugr2/DBLP_processed.zip?dl=0)
* Last.fm - [Dropbox](https://www.dropbox.com/s/jvlbs09pz6zwcka/LastFM_processed.zip?dl=0)
The GloVe word vectors are obtained from [GloVe](https://nlp.stanford.edu/projects/glove/). Here is [the direct link](http://nlp.stanford.edu/data/glove.6B.zip) 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`
2. 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}
}
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f82fdf9ca85a2c30, llm:Repository topics: graph-neural-network, heterogeneous-graph, heterogeneous-graph-neural-network, heterogeneous-network, network-embedding; README: 'Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding' with PyTorch/DGL implementation and datasets.
matched fp:f82fdf9ca85a2c30, llm:Repository topics: graph-neural-network, heterogeneous-graph, heterogeneous-graph-neural-network, heterogeneous-network, network-embedding; README: 'Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding' with PyTorch/DGL implementation and datasets.