https://github.com/trustagi-lab/udagcn
Python implementation of "Unsupervised Domain Adaptive Graph Convolutional Networks", WWW-20.
https://github.com/trustagi-lab/udagcn
domain-adaptation graph-neural-networks
Last synced: 6 months ago
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Python implementation of "Unsupervised Domain Adaptive Graph Convolutional Networks", WWW-20.
- Host: GitHub
- URL: https://github.com/trustagi-lab/udagcn
- Owner: TrustAGI-Lab
- License: mit
- Created: 2020-06-27T16:36:34.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-05-13T03:30:42.000Z (over 4 years ago)
- Last Synced: 2025-03-27T14:55:31.561Z (7 months ago)
- Topics: domain-adaptation, graph-neural-networks
- Language: Python
- Homepage:
- Size: 29.3 KB
- Stars: 55
- Watchers: 0
- Forks: 21
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Unsupervised Domain Adaptive Graph Convolutional Networks
This repository contains the author's implementation in PyTorch for the paper "Unsupervised Domain Adaptive Graph Convolutional Networks", WWW-20.
## Dependencies
- Python (>=3.6)
- Torch (>=1.2.0)
- numpy (>=1.16.4)
- torch_scatter (>= 1.3.0)
- torch_geometric (>= 1.3.0)
## Datasets
The data folder includes different domain data. The preprocessed data can be found in [Google Drive](https://drive.google.com/file/d/1DzQ3QN9yjQxU4vtYkXyCiJKFw7oCCPSM/view?usp=sharing).
The orginal datasets can be founded from [here](https://www.aminer.cn/citation).
## Implementation
Here we provide the implementation of UDA-GCN, along with two domain datasets. The repository is organised as follows:
- `data/` contains the necessary dataset files for DBLP domain and ACM domian(can be found in [Google Drive](https://drive.google.com/file/d/1DzQ3QN9yjQxU4vtYkXyCiJKFw7oCCPSM/view?usp=sharing));
- `dual_gnn/` contains the implementation of the Global GCN and Local GCN;
Finally, `UDAGCN_demo.py` puts all of the above together and can be used to execute a full training run on the datasets.
## Process
- Place the datasets in `data/`
- Change the `dataset` in `UDAGCN_demo.py` .
- Training/Testing:
```bash
python UDAGCN_demo.py
```
# Citation
```
@inproceedings{wu2020UDAGCN
author={Man Wu and Shirui Pan and Chuan Zhou and Xiaojun Chang and Xingquan Zhu},
title={Unsupervised Domain Adaptive Graph Convolutional Networks},
journal={{WWW} '20: The Web Conference},
year={2020}
}
```