Bag of Tricks for Graph Neural Networks
Project description
gtrick: Bag of Tricks for Graph Neural Networks
Trick is all you need.
Trick
Trick | Example | Task | Reference |
---|---|---|---|
Virtual Node | DGL PyG |
graph | Neural Message Passing for Quantum Chemistry |
FLAG | DGL PyG |
node* graph |
Robust Optimization as Data Augmentation for Large-scale Graphs |
Benchmark
The results listed below are implemented by PyG. You can find the results of DGL in DGL Benchmark.
Graph Property Prediction: ogbg-molhiv
Trick | GCN | GIN |
---|---|---|
— | 0.7690 ± 0.0053 | 0.7778 ± 0.0130 |
+Virtual Node | 0.7581 ± 0.0135 | 0.7713 ± 0.0036 |
+FLAG | 0.7627 ± 0.0124 | 0.7764 ± 0.0083 |
Node Property Prediction: ogbn-arxiv
Trick | GCN | SAGE |
---|---|---|
— | 0.7152 ± 0.0024 | 0.7153 ± 0.0028 |
+FLAG | 0.7187 ± 0.0020 | 0.7206 ± 0.0013 |
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