EGI

Source code for "Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization", 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