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Hongzhi (Steve), Chen 704bcaf6dd | 1 year ago | |
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README.md | 1 year ago | |
main.py | 1 year ago | |
model.py | 1 year ago |
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
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
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.
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 |
Training a Base predictor and using Correct&Smooth which follows the original hyperparameters on different datasets.
python main.py --dropout 0.5
python main.py --pretrain --correction-adj DA --smoothing-adj AD --autoscale
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
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
Linear | Plain Linear + C&S | |
---|---|---|
Results(Author) | 52.5 | 71.26 |
Results(DGL) | 52.48 | 71.26 |
Plain Linear | Plain Linear + C&S | |
---|---|---|
Results(Author) | 47.67 | 82.34 |
Results(DGL) | 47.65 | 82.86 |
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 |
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