Graph Classification on IMDB-M (test)
55.28Accuracyatt-Pooling-NLSFs
Evaluation Results
| Method | Links | |
|---|---|---|
| att-Pooling-NLSFsArchitecture=Pooling2024.06 | 55.28 | |
| ARMA2024.06 | 53.6 | |
| LGAN-resresidual=true2025.12 | 53.5 | |
| LGANresidual=false2025.12 | 53.3 | |
| TopoGCL2025.12 | 52.81 | |
| CW Networkscategory=k-WL-based2025.12 | 52.7 | |
| ChebNetII2024.06 | 52.69 | |
| att-Graph-level NLSFsArchitecture=Graph-level2024.06 | 52.49 | |
| GraphTCL2025.12 | 52.4 | |
| GINcategory=GNNs2025.12 | 52.3 | |
| CayleyNet2024.06 | 51.89 | |
| TFGW2025.12 | 50.67 | |
| 2-WLcategory=Kernels2025.12 | 50.6 | |
| PPGNcategory=k-WL-based2025.12 | 50.5 | |
| GCN2025.12 | 50.47 | |
| DiffPool2024.06 | 50.43 | |
| GCN2024.06 | 50.28 | |
| delta-2-LWLcategory=k-WL-based2025.12 | 50.2 | |
| GIN2025.12 | 50.2 | |
| ChebyNet2025.12 | 50.2 | |
| APPNP2025.12 | 50.13 | |
| GAT2025.12 | 49.93 | |
| ChebNet2024.06 | 49.82 | |
| GPRGNN2025.12 | 49.8 | |
| 1-2-3 GNNcategory=k-WL-based2025.12 | 49.5 | |
| GPFPre-training Strategy=GCC (MoCo), Tuning Strategy=GPF2022.09 | 49.33 | |
| GPFPre-training Strategy=GCC (E2E), Tuning Strategy=GPF2022.09 | 49.17 | |
| FTPre-training Strategy=GCC (E2E), Tuning Strategy=FT2022.09 | 49.07 | |
| OT-GNN2025.12 | 49 | |
| WL-Kernel2025.12 | 48.53 | |
| FTPre-training Strategy=GCC (MoCo), Tuning Strategy=FT2022.09 | 48.07 | |
| DGCNNcategory=GNNs2025.12 | 47.8 | |
| GPRGNN2024.06 | 47.07 | |
| SAGE2024.06 | 46.94 | |
| WL2024.06 | 46.63 | |
| GAT2024.06 | 45.67 | |
| PATCHY-SANcategory=GNNs2025.12 | 45.2 | |
| APPNP2024.06 | 44.36 | |
| GK2024.06 | 44.19 | |
| PGOT12025.12 | 43.07 | |
| DIFFPOOL2025.12 | 42.08 | |
| SPcategory=Kernels2025.12 | 40.5 | |
| DropGIN2025.12 | 37.33 | |
| DCNNcategory=GNNs2025.12 | 33.5 | |
| SP-Kernel2025.12 | 28.74 |