Awesome
CPF
The official code of WWW2021 paper: Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
Getting Started
Requirements
- Python version >= 3.6
- PyTorch version >= 1.7.1
- DGL
- Optuna (optional)
Usage
Quick start
- use
python train_dgl.py --dataset=XXX --teacher=XXX
to run teacher model. - use
python spawn_worker.py --dataset=XXX --teacher=XXX
to run student model, we provide our hyper-parameters setting as reported in our paper, and an AutoML version for hyper-parameters search. (Our code supports Optuna to search best hyper-parameters for knowledge distillation. You can use--automl
to run Optuna code.)
Add your own datasets
You can add your own datasets to folder data
, the formats should accord to DGL requirements.
Add your own models
You can add your own teacher or student model by adding them into folder models
, and following the format of model run.
Results
There are some results on GCN teacher model, with different datasets and student varients. More results can be seen in our paper.
Datasets | GCN (Teacher) | CPF-ind (Student) | CPF-tra (Student) | improvement |
---|---|---|---|---|
Cora | 0.8244 | 0.8576 | 0.8567 | 4.0% |
Citeseer | 0.7110 | 0.7619 | 0.7652 | 7.6% |
Pubmed | 0.7804 | 0.8080 | 0.8104 | 3.8% |
A-Computers | 0.8318 | 0.8443 | 0.8443 | 1.5% |
A-Photo | 0.9072 | 0.9317 | 0.9248 | 2.7% |
Benchmark Rankings
There are results use several models run on different benchmark datasets. Our experiments settings are available in the following form and the pwc.conf.yaml
file. For simple usage, please try AutoML for hyper-parameters search.
Note:
- Remember to change the load data function to load_citation when running public split benchmarks.
- Use original load data function when running AMZ datasets, remember to slice the test sets to corresponding size.
Cite
Please cite our paper if you use this code in your own work:
@inproceedings{yang2021extract,
title={Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework},
author={Cheng Yang and Jiawei Liu and Chuan Shi},
booktitle={Proceedings of The Web Conference 2021 (WWW ’21)},
publisher={ACM},
year={2021}
}
Contact Us
Please open an issue or contact Liu_Jiawei@bupt.edu.cn with any questions.