Official Pytorch Implementation of "AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models" (ICLR 2021)
Please refer to openreview (ICLR 2021) to look into the details of our paper.
python3.6
cuda11.0
torch1.7.1
python main.py --trainsize 20 --dataset citeseer --niter 5 --nseed 20 --model GCN --dropout 0.5 --reg 5e-4
python main.py --trainsize 20 --dataset citeseer --niter 5 --nseed 20 --model APPNP --dropout 0.5 --early 1 --patience 300 --max 500 --reg 5e-3
python main.py --trainsize 20 --dataset citeseer --niter 5 --nseed 20 --model AdaGCN --layers 15 --hid_AdaGCN 5000 --dropout 0.0 --weight_decay 1e-3 --early 1 --patience 300 --max 500 --reg 5e-3
python main.py --trainsize 20 --dataset cora_ml --niter 5 --nseed 20 --model AdaGCN --layers 12 --hid_AdaGCN 5000 --dropout 0.0 --weight_decay 1e-4 --early 1 --patience 300 --max 500 --reg 5e-3
python main.py --trainsize 20 --dataset pubmed --niter 5 --nseed 20 --model AdaGCN --layers 20 --hid_AdaGCN 5000 --dropout 0.2 --weight_decay 1e-4 --early 1 --patience 300 --max 500 --reg 5e-3
python main.py --trainsize 20 --dataset ms_academic --niter 5 --nseed 20 --model AdaGCN --layers 5 --hid_AdaGCN 3000 --dropout 0.2 --weight_decay 1e-4 --early 1 --patience 300 --max 500 --reg 5e-3
Results:
Dataset | Average Accuracy | Std |
---|---|---|
Citeseer | 76.68 | 0.20 |
Cora-ML | 85.97 | 0.20 |
PubMed | 79.95 | 0.21 |
MS Academic | 93.17 | 0.07 |
Our code is directly adapted from PPNP paper Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019) github: https://github.com/klicperajo/ppnp.
Please refer to [email protected] in case you have any questions.
Please cite our paper if you use the model or this code in your own work:
@inproceedings{sun2019adagcn,
title={Adagcn: Adaboosting graph convolutional networks into deep models},
author={Sun, Ke and Lin, Zhouchen and Zhu, Zhanxing},
booktitle={International Conference on Learning Representations (ICLR)},
year={2021}
}