https://github.com/gentlezhu/egi
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization (NeurIPS 21')
https://github.com/gentlezhu/egi
domain-adaptation graph-neural-netowrks pre-training transfer-learning
Last synced: about 1 month ago
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Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization (NeurIPS 21')
- Host: GitHub
- URL: https://github.com/gentlezhu/egi
- Owner: GentleZhu
- Created: 2021-02-19T16:36:24.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-12-09T16:37:43.000Z (almost 4 years ago)
- Last Synced: 2023-02-28T04:17:00.287Z (over 2 years ago)
- Topics: domain-adaptation, graph-neural-netowrks, pre-training, transfer-learning
- Language: Python
- Homepage:
- Size: 240 KB
- Stars: 19
- Watchers: 2
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# EGI
Source code for ["Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization"](https://proceedings.neurips.cc/paper/2021/file/0dd6049f5fa537d41753be6d37859430-Paper.pdf), published in NeurIPS 2021.
If you find our paper useful, please consider cite the following paper.
```
@article{zhu2020transfer,
title={Transfer learning of graph neural networks with ego-graph information maximization},
author={Zhu, Qi and Yang, Carl and Xu, Yidan and Wang, Haonan and Zhang, Chao and Han, Jiawei},
journal={arXiv preprint arXiv:2009.05204},
year={2020}
}
```
## Requirements
Please use old version of DGL library (0.4.3) to run the original code.
### CPU version
```
pip install dgl==0.4.3
```
### DGL GPU version (recommened)
Change your cuda version accordingly.
```
pip install dgl-cu101==0.4.3
```
## Model specifications
EGI model can be found under models/subgi.py, we call EGI as SubGI when code is developed. The default encoder arch is GIN as you will see in the code. To run the airport data, see example below
```
python run_airport.py --file-path=data/usa-airports.edgelist --label-path=data/labels-usa-airports.txt --n-dgi-epochs=100 --n-hidden=32 --self-loop --gpu=0 --n-layers=1 --dgi-lr=0.01 --model-id=2 --model-type=2
```
We also provide the code to run DGI on the dataset as below:
```
python run_airport.py --file-path=data/usa-airports.edgelist --label-path=data/labels-usa-airports.txt --n-dgi-epochs=100 --n-hidden=32 --self-loop --gpu=0 --n-layers=1 --dgi-lr=0.001 --model-id=2 --model-type=0
```
## Computer the EGI gap term
### from edgelist
```
python compute_bound_filepath.py --args.file-path=data/europe-aiports.edgelist --args.label-path=data/usa-aiports.edgelist
```
### from pickle file for synthetic experiment
```
python compute_bound_pickle.py --args.file-path=data/barabasi_small_graphs_full.pkl --args.label-path=data/forest_fire_graphs_full.pkl
```