Graph Regression on OGBG-PCQM4M v1 (val)
0.1197MAEMPNN + ER
Evaluation results
| Method | Links | |
|---|---|---|
| MPNN + ER#Layers=32, Noisy Nodes=Yes, Random Features=No2022.06 | 0.1197 | |
| MPNN + Conformers#Layers=32, Noisy Nodes=Yes, Random Features=No2022.06 | 0.1212 | |
| MPNN + ER#Layers=32, Noisy Nodes=No, Random Features=No2022.06 | 0.1214 | |
| MPNN#Layers=32, Noisy Nodes=Yes, Random Features=Yes2022.06 | 0.1216 | |
| MPNN#Layers=50, Noisy Nodes=Yes, Random Features=No2022.06 | 0.1218 | |
| MPNN#Layers=32, Noisy Nodes=Yes, Random Features=No2022.06 | 0.1222 | |
| Graphormer2022.06 | 0.1234 | |
| MPNN#Layers=50, Noisy Nodes=No, Random Features=No2022.06 | 0.1236 | |
| MPNN#Layers=32, Noisy Nodes=No, Random Features=Yes2022.06 | 0.1237 | |
| MPNN#Layers=16, Noisy Nodes=Yes, Random Features=No2022.06 | 0.1249 |