DGL Implementation of CorrectAndSmooth

This DGL example implements the GNN model proposed in the paper Combining Label Propagation and Simple Models Out-performs Graph Neural Networks. For the original implementation, see here.

Contributor: xnuohz

Requirements

The codebase is implemented in Python 3.7. For version requirement of packages, see below.

dgl 0.6.0.post1
torch 1.7.0
ogb 1.3.0

The graph datasets used in this example

Open Graph Benchmark(OGB). Dataset summary:

Dataset	#Nodes	#Edges	#Node Feats	Metric
ogbn-arxiv	169,343	1,166,243	128	Accuracy
ogbn-products	2,449,029	61,859,140	100	Accuracy

Usage

Training a Base predictor and using Correct&Smooth which follows the original hyperparameters on different datasets.

ogbn-arxiv

• MLP + C&S

python main.py --dropout 0.5
python main.py --pretrain --correction-adj DA --smoothing-adj AD

• Linear + C&S

python main.py --model linear --dropout 0.5 --epochs 1000
python main.py --model linear --pretrain --correction-alpha 0.8 --smoothing-alpha 0.6 --correction-adj AD

• GAT + C&S

python main.py --model gat --epochs 2000 --lr 0.002 --dropout 0.75
python main.py --model gat --pretrain

ogbn-products

• Linear + C&S

python main.py --dataset ogbn-products --model linear --dropout 0.5 --epochs 1000 --lr 0.1
python main.py --dataset ogbn-products --model linear --pretrain --correction-alpha 0.6 --smoothing-alpha 0.9

Performance

ogbn-arxiv

	MLP	MLP + C&S	Linear	Linear + C&S	GAT	GAT + C&S
Results(Author)	55.58	68.72	51.06	70.24	73.28	73.64
Results(DGL)	56.12	68.63	52.49	71.69	72.59	73.30

ogbn-products

	Linear	Linear + C&S
Results(Author)	47.67	82.34
Results(DGL)	47.71	79.57

Speed

ogb-arxiv	Time	GPU Memory	Params
Author, Linear + C&S	6.3 * 10 ^ -3	1,248M	5,160
DGL, Linear + C&S	5.6 * 10 ^ -3	1,252M	5,160