Node Classification On Pubmed 005
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Accuracy |
|---|---|
| graph-attention-networks | 50.4% |
| lanczosnet-multi-scale-deep-graph | 66% |
| lanczosnet-multi-scale-deep-graph | 68.8 ± 5.6 |
| convolutional-neural-networks-on-graphs-with | 48.2% |
| mutual-teaching-for-graph-convolutional | 69.5% |
| break-the-ceiling-stronger-multi-scale-deep | 69.45% |
| gated-graph-sequence-neural-networks | 63.3% |
| convolutional-networks-on-graphs-for-learning | 63.2% |
| break-the-ceiling-stronger-multi-scale-deep | 72.57% |
| diffusion-convolutional-neural-networks | 66.7% |
| break-the-ceiling-stronger-multi-scale-deep | 70.04% |
| inductive-representation-learning-on-large | 53.0% |
| break-the-ceiling-stronger-multi-scale-deep | 68.99% |
| view-consistent-heterogeneous-network-on | 74.3% |