DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Requirements
- Python 3.6.2
- For the other packages, please refer to the requirements.txt.
Usage
To run the demo: sh run.sh
All scripts of different models with parameters for Cora, Citeseer and Pubmed are in scripts folder. You can reproduce the results by:
pip install -r requirements.txt
sh scripts/supervised/cora_IncepGCN.sh
Data
The data format is same as GCN. We provide three benchmark datasets as examples (see data folder). We use the public dataset splits provided by Planetoid. The semi-supervised setting strictly follows GCN, while the full-supervised setting strictly follows FastGCN and ASGCN.
Benchmark Results
For the details of backbones in Tables, please refer to the Appendix B.2 in the paper. All results are obtained on GPU (CUDA Version 9.0.176).
Full-supervised Setting Results
The following table demonstrates the testing accuracy (%) comparisons on different backbones and layers w and w/o DropEdge.
| Dataset | Backbone | 2 layers | 4 layers | 8 layers | 16 layers | 32 layers | 64 layers | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Orignal | DropEdge | Orignal | DropEdge | Orignal | DropEdge | Orignal | DropEdge | Orignal | DropEdge | Orignal | DropEdge | ||
| Cora | GCN | 86.10 | 86.50 | 85.50 | 87.60 | 78.70 | 85.80 | 82.10 | 84.30 | 71.60 | 74.60 | 52.00 | 53.20 |
| ResGCN | - | - | 86.00 | 87.00 | 85.40 | 86.90 | 85.30 | 86.90 | 85.10 | 86.80 | 79.80 | 84.80 | |
| JKNet | - | - | 86.90 | 87.70 | 86.70 | 87.80 | 86.20 | 88.00 | 87.10 | 87.60 | 86.30 | 87.90 | |
| IncepGCN | - | - | 85.60 | 87.90 | 86.70 | 88.20 | 87.10 | 87.70 | 87.40 | 87.70 | 85.30 | 88.20 | |
| GraphSage | 87.80 | 88.10 | 87.10 | 88.10 | 84.30 | 87.10 | 84.10 | 84.50 | 31.90 | 32.20 | 31.90 | 31.90 | |
| Citeseer | GCN | 75.90 | 78.70 | 76.70 | 79.20 | 74.60 | 77.20 | 65.20 | 76.80 | 59.20 | 61.40 | 44.60 | 45.60 |
| ResGCN | - | - | 78.90 | 78.80 | 77.80 | 78.80 | 78.20 | 79.40 | 74.40 | 77.90 | 21.20 | 75.30 | |
| JKNet | - | - | 79.10 | 80.20 | 79.20 | 80.20 | 78.80 | 80.10 | 71.70 | 80.00 | 76.70 | 80.00 | |
| IncepGCN | - | - | 79.50 | 79.90 | 79.60 | 80.50 | 78.50 | 80.20 | 72.60 | 80.30 | 79.00 | 79.90 | |
| GraphSage | 78.40 | 80.00 | 77.30 | 79.20 | 74.10 | 77.10 | 72.90 | 74.50 | 37.00 | 53.60 | 16.90 | 25.10 | |
| Pubmed | GCN | 90.20 | 91.20 | 88.70 | 91.30 | 90.10 | 90.90 | 88.10 | 90.30 | 84.60 | 86.20 | 79.70 | 79.00 |
| ResGCN | - | - | 90.70 | 90.70 | 89.60 | 90.50 | 89.60 | 91.00 | 90.20 | 91.10 | 87.90 | 90.20 | |
| JKNet | - | - | 90.50 | 91.30 | 90.60 | 91.20 | 89.90 | 91.50 | 89.20 | 91.30 | 90.60 | 91.60 | |
| IncepGCN | - | - | 89.90 | 91.60 | 90.20 | 91.50 | 90.80 | 91.30 | OOM | 90.50 | OOM | 90.00 | |
| GraphSage | 90.10 | 90.70 | 89.40 | 91.20 | 90.20 | 91.70 | 83.50 | 87.80 | 41.30 | 47.90 | 40.70 | 62.30 | |
Semi-supervised Setting Results
The following table demonstrates the testing accuracy (%) comparisons on different backbones and layers w and w/o DropEdge.
| Dataset | Method | 2 layers | 4 laysers | 8 layers | 16 layers | 32 layers | 64 layers | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Orignal | DropEdge | Orignal | DropEdge | Orignal | DropEdge | Orignal | DropEdge | Orignal | DropEdge | Orignal | DropEdge | ||
| Cora | GCN | 81.10 | 82.80 | 80.40 | 82.00 | 69.50 | 75.80 | 64.90 | 75.70 | 60.30 | 62.50 | 28.70 | 49.50 |
| ResGCN | - | - | 78.80 | 83.30 | 75.60 | 82.80 | 72.20 | 82.70 | 76.60 | 81.10 | 61.10 | 78.90 | |
| JKNet | - | - | 80.20 | 83.30 | 80.70 | 82.60 | 80.20 | 83.00 | 81.10 | 82.50 | 71.50 | 83.20 | |
| IncepGCN | - | - | 77.60 | 82.90 | 76.50 | 82.50 | 81.70 | 83.10 | 81.70 | 83.10 | 80.00 | 83.50 | |
| Citeseer | GCN | 70.80 | 72.30 | 67.60 | 70.60 | 30.20 | 61.40 | 18.30 | 57.20 | 25.00 | 41.60 | 20.00 | 34.40 |
| ResGCN | - | - | 70.50 | 72.20 | 65.00 | 71.60 | 66.50 | 70.10 | 62.60 | 70.00 | 22.10 | 65.10 | |
| JKNet | - | - | 68.70 | 72.60 | 67.70 | 71.80 | 69.80 | 72.60 | 68.20 | 70.80 | 63.40 | 72.20 | |
| IncepGCN | - | - | 69.30 | 72.70 | 68.40 | 71.40 | 70.20 | 72.50 | 68.00 | 72.60 | 67.50 | 71.00 | |
| Pubmed | GCN | 79.00 | 79.60 | 76.50 | 79.40 | 61.20 | 78.10 | 40.90 | 78.50 | 22.40 | 77.00 | 35.30 | 61.50 |
| ResGCN | - | - | 78.60 | 78.80 | 78.10 | 78.90 | 75.50 | 78.00 | 67.90 | 78.20 | 66.90 | 76.90 | |
| JKNet | - | - | 78.00 | 78.70 | 78.10 | 78.70 | 72.60 | 79.10 | 72.40 | 79.20 | 74.50 | 78.90 | |
| IncepGCN | - | - | 77.70 | 79.50 | 77.90 | 78.60 | 74.90 | 79.00 | OOM | OOM | OOM | OOM | |
Change Log
- 2020-03-04: Support for
tensorboardand added an example insrc/train_new.py. Thanks for MihailSalnikov. - 2019-10-11: Support both full-supervised and semi-supervised task setting for
Cora,CiteseerandPubmed. See--task_typeoption.
References
@inproceedings{
rong2020dropedge,
title={DropEdge: Towards Deep Graph Convolutional Networks on Node Classification},
author={Yu Rong and Wenbing Huang and Tingyang Xu and Junzhou Huang},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Hkx1qkrKPr}
}
1 Jan 06, 2022
4.1k Jan 03, 2023
23 Jul 02, 2022
18 Dec 31, 2022
[email protected]">
38 Nov 22, 2022
23 Oct 19, 2022
701 Dec 30, 2022
67 Dec 31, 2022
1.8k Jan 07, 2023
3 Sep 18, 2022
79 Dec 30, 2022
134 Dec 08, 2022
7 Sep 26, 2022
302 Dec 23, 2022
147 Dec 31, 2022
7 Apr 16, 2022
248 Jan 02, 2023
3k Dec 31, 2022
50 Oct 08, 2021
18 Jun 13, 2022