/GFKD

The implementation for the work "Graph-Free Knowledge Distillation for Graph Neural Networks".

Primary LanguagePython

Graph-Free Knowledge Distillation for Graph Neural Networks

This repository is a PyTorch implementation of [Graph-Free Knowledge Distillation for Graph Neural Networks].

Requirements

The code was tested on
Python 3.6
torch 1.6.0
torchvision 0.7.0
DGL 0.4.2

torch 1.2.0 with torchvision 0.4.0 and torch 1.4.0 with torchvision 0.5.0 should also work.

Running the code

To distill knowledge from GCN5-64 to student GCN-3-32 or GIN-3-32 with the generated fake data (from teacher GCN5-64), run the command:
sh train_kd_gcn.sh

To re-generate fake graphs from teacher GCN-5-64, run the command:
sh generate_fake_graphs.sh

Note

When you use a different teacher GNN or dataset, you need to re-tune the hyper-parameters (i.e., bn_reg_scale or/and onehot_cof).

Citation

@inproceedings{deng2021graph,
  title={Graph-Free Knowledge Distillation for Graph Neural Networks},
  author={Deng, Xiang and Zhang, Zhongfei},
  booktitle = {The 30th International Joint Conference on Artificial Intelligence},
  year={2021}
}