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 tensorboard and added an example in src/train_new.py. Thanks for MihailSalnikov.
• 2019-10-11: Support both full-supervised and semi-supervised task setting for Cora, Citeseer and Pubmed. See --task_type option.

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}
}