Each model is hosted in their own folders. Please read their README.md to see how to run them.
To understand step-by-step how these models are implemented in DGL. Check out our tutorials
Here is a summary of the model accuracy and training speed. Our testbed is Amazon EC2 p3.2x instance (w/ V100 GPU).
| Model | Reported Accuracy |
DGL Accuracy |
Author's training speed (epoch time) | DGL speed (epoch time) | Improvement |
|---|---|---|---|---|---|
| GCN | 81.5% | 81.0% | 0.0051s (TF) | 0.0031s | 1.64x |
| GAT | 83.0% | 83.9% | 0.0982s (TF) | 0.0113s | 8.69x |
| SGC | 81.0% | 81.9% | n/a | 0.0008s | n/a |
| TreeLSTM | 51.0% | 51.72% | 14.02s (DyNet) | 3.18s | 4.3x |
| R-GCN (classification) |
73.23% | 73.53% | 0.2853s (Theano) | 0.0075s | 38.2x |
| R-GCN (link prediction) |
0.158 | 0.151 | 2.204s (TF) | 0.453s | 4.86x |
| JTNN | 96.44% | 96.44% | 1826s (Pytorch) | 743s | 2.5x |
| LGNN | 94% | 94% | n/a | 1.45s | n/a |
| DGMG | 84% | 90% | n/a | 238s | n/a |