Graph Regression on ZINC
0.077MSECIN++
Evaluation Results
| Method | Links | |
|---|---|---|
| CIN++Edge Features=true2023.08 | 0.077 | |
| CINEdge Features=true2023.08 | 0.079 | |
| PathNNEdge Features=true2023.08 | 0.09 | |
| PINEdge Features=true2023.08 | 0.096 | |
| GSNEdge Features=true2023.08 | 0.115 | |
| CINEdge Features=false2023.08 | 0.115 | |
| PINEdge Features=false2023.08 | 0.139 | |
| GSNEdge Features=false2023.08 | 0.14 | |
| HIMPEdge Features=true2023.08 | 0.151 | |
| DGNEdge Features=true2023.08 | 0.168 | |
| PNAEdge Features=true2023.08 | 0.188 | |
| DGNEdge Features=false2023.08 | 0.219 | |
| GINEdge Features=true2023.08 | 0.252 | |
| PNAEdge Features=false2023.08 | 0.32 | |
| GPF-LoRAPQuantization Framework=A2Q, Model Architecture=GIN2026.01 | 0.361 | |
| GatedGCNEdge Features=true2023.08 | 0.363 | |
| GPF-plusQuantization Framework=A2Q, Model Architecture=GIN2026.01 | 0.399 | |
| No PromptingQuantization Framework=A2Q, Model Architecture=GIN2026.01 | 0.4 | |
| GINEdge Features=false2023.08 | 0.408 | |
| No PromptingQuantization Framework=FP32, Model Architecture=GIN2026.01 | 0.414 | |
| GatedGCNEdge Features=false2023.08 | 0.422 | |
| GATEdge Features=false2023.08 | 0.463 | |
| No PromptingQuantization Framework=FP32, Model Architecture=GAT2026.01 | 0.463 | |
| GCNEdge Features=false2023.08 | 0.469 | |
| No PromptingQuantization Framework=FP32, Model Architecture=GCN2026.01 | 0.469 | |
| GPF-LoRAPQuantization Framework=A2Q, Model Architecture=GCN2026.01 | 0.482 | |
| No PromptingQuantization Framework=A2Q, Model Architecture=GCN2026.01 | 0.504 | |
| GPF-plusQuantization Framework=A2Q, Model Architecture=GCN2026.01 | 0.506 | |
| GPF-LoRAPQuantization Framework=DQ, Model Architecture=GAT2026.01 | 0.508 | |
| GPF-LoRAPQuantization Framework=DQ, Model Architecture=GIN2026.01 | 0.512 | |
| GPF-LoRAPQuantization Framework=QAT, Model Architecture=GIN2026.01 | 0.528 | |
| GPF-plusQuantization Framework=DQ, Model Architecture=GCN2026.01 | 0.534 | |
| No PromptingQuantization Framework=DQ, Model Architecture=GIN2026.01 | 0.54 | |
| No PromptingQuantization Framework=QAT, Model Architecture=GIN2026.01 | 0.555 | |
| GPF-plusQuantization Framework=DQ, Model Architecture=GIN2026.01 | 0.555 | |
| No PromptingQuantization Framework=DQ, Model Architecture=GAT2026.01 | 0.556 | |
| GPF-plusQuantization Framework=QAT, Model Architecture=GIN2026.01 | 0.569 | |
| GPF-LoRAPQuantization Framework=DQ, Model Architecture=GCN2026.01 | 0.57 | |
| GPF-plusQuantization Framework=DQ, Model Architecture=GAT2026.01 | 0.589 | |
| GPF-LoRAPQuantization Framework=A2Q, Model Architecture=GAT2026.01 | 0.628 | |
| No PromptingQuantization Framework=DQ, Model Architecture=GCN2026.01 | 0.657 | |
| No PromptingQuantization Framework=A2Q, Model Architecture=GAT2026.01 | 0.674 | |
| GPF-LoRAPQuantization Framework=QAT, Model Architecture=GAT2026.01 | 0.685 | |
| GPF-plusQuantization Framework=A2Q, Model Architecture=GAT2026.01 | 0.685 | |
| GPF-LoRAPQuantization Framework=QAT, Model Architecture=GCN2026.01 | 0.69 | |
| GPF-plusQuantization Framework=QAT, Model Architecture=GAT2026.01 | 0.693 | |
| No PromptingQuantization Framework=QAT, Model Architecture=GAT2026.01 | 0.702 | |
| No PromptingQuantization Framework=QAT, Model Architecture=GCN2026.01 | 0.707 | |
| GPF-plusQuantization Framework=QAT, Model Architecture=GCN2026.01 | 0.733 |