Node Classification On Pubmed 003
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Accuracy |
|---|---|
| view-consistent-heterogeneous-network-on | 71.8% |
| diffusion-convolutional-neural-networks | 60.9% |
| break-the-ceiling-stronger-multi-scale-deep | 71.11% |
| gated-graph-sequence-neural-networks | 55.8% |
| break-the-ceiling-stronger-multi-scale-deep | 62.61% |
| graph-attention-networks | 50.9% |
| convolutional-neural-networks-on-graphs-with | 45.3% |
| mutual-teaching-for-graph-convolutional | 65.5% |
| break-the-ceiling-stronger-multi-scale-deep | 61.94% |
| break-the-ceiling-stronger-multi-scale-deep | 68.12% |
| lanczosnet-multi-scale-deep-graph | 61% |
| convolutional-networks-on-graphs-for-learning | 56.2% |
| lanczosnet-multi-scale-deep-graph | 60.4 ± 8.6 |
| inductive-representation-learning-on-large | 45.4% |