wehub-resource-syncDGL Implementation of DeeperGCN
This DGL example implements the GNN model proposed in the paper DeeperGCN: All You Need to Train Deeper GCNs. 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:
Graph Property Prediction
| Dataset | #Graphs | #Node Feats | #Edge Feats | Metric |
|---|---|---|---|---|
| ogbg-molhiv | 41,127 | 9 | 3 | ROC-AUC |
Usage
Train a model which follows the original hyperparameters on different datasets.
# ogbg-molhiv
python main.py --gpu 0 --learn-beta
Performance
- Table 6: Numbers associated with "Table 6" are the ones from table 6 in the paper.
- Author: Numbers associated with "Author" are the ones we got by running the original code.
- DGL: Numbers associated with "DGL" are the ones we got by running the DGL example.
| Dataset | ogbg-molhiv |
|---|---|
| Results(Table 6) | 0.786 |
| Results(Author) | 0.781 |
| Results(DGL) | 0.778 |
Speed
| Dataset | ogbg-molhiv |
|---|---|
| Results(Author) | 11.833 |
| Results(DGL) | 8.965 |