correct_and_smooth

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

Limitations

Spectral and Diffusion Embeddings used by the authors for feature augmentation are not currently implemented. Without these feature augmentations only the "Plain" (without feature augmentations) results from the authors can be replicated.

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

• Plain MLP + C&S

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

• Plain Linear + C&S

python main.py --model linear --dropout 0.5 --epochs 1000
python main.py --model linear --pretrain --correction-alpha 0.87 --smoothing-alpha 0.81 --correction-adj AD --autoscale

ogbn-products

• Plain 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 1. --smoothing-alpha 0.9

Performance

ogbn-arxiv

	Linear	Plain Linear + C&S
Results(Author)	52.5	71.26
Results(DGL)	52.48	71.26

ogbn-products

	Plain Linear	Plain Linear + C&S
Results(Author)	47.67	82.34
Results(DGL)	47.65	82.86

Speed

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