Graph Classification on MIT AICURES (test)
0.6725AUPRCLDAM
Evaluation results
| Method | Links | |
|---|---|---|
| LDAMGNN Architecture=ML-MPNN2021.04 | 0.6725 | |
| SOAPGNN Architecture=GINE2021.04 | 0.6639 | |
| SOAPGNN Architecture=MPNN2021.04 | 0.6547 | |
| SOAPGNN Architecture=ML-MPNN2021.04 | 0.6503 | |
| LDAMGNN Architecture=MPNN2021.04 | 0.6489 | |
| CB-CEGNN Architecture=MPNN2021.04 | 0.6308 | |
| CEGNN Architecture=MPNN2021.04 | 0.6282 | |
| CEGNN Architecture=ML-MPNN2021.04 | 0.6101 | |
| FocalGNN Architecture=MPNN2021.04 | 0.5875 | |
| MinMaxGNN Architecture=ML-MPNN2021.04 | 0.5832 | |
| MinMaxGNN Architecture=MPNN2021.04 | 0.5774 | |
| CB-CEGNN Architecture=GINE2021.04 | 0.5655 | |
| AUC-MGNN Architecture=MPNN2021.04 | 0.5542 | |
| MinMaxGNN Architecture=GINE2021.04 | 0.5292 | |
| LDAMGNN Architecture=GINE2021.04 | 0.5236 | |
| FastAPGNN Architecture=ML-MPNN2021.04 | 0.5174 | |
| AUC-MGNN Architecture=GINE2021.04 | 0.5149 | |
| FocalGNN Architecture=GINE2021.04 | 0.5143 | |
| CEGNN Architecture=GINE2021.04 | 0.5037 | |
| CB-CEGNN Architecture=ML-MPNN2021.04 | 0.4903 | |
| FastAPGNN Architecture=GINE2021.04 | 0.4777 | |
| FocalGNN Architecture=ML-MPNN2021.04 | 0.4718 | |
| FastAPGNN Architecture=MPNN2021.04 | 0.4518 | |
| AUC-MGNN Architecture=ML-MPNN2021.04 | 0.4429 | |
| SmoothAPGNN Architecture=ML-MPNN2021.04 | 0.4212 | |
| SmoothAPGNN Architecture=MPNN2021.04 | 0.4081 | |
| SmoothAPGNN Architecture=GINE2021.04 | 0.2899 |