Node Classification On Cora 05
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Accuracy |
|---|---|
| inductive-representation-learning-on-large | 37.5% |
| break-the-ceiling-stronger-multi-scale-deep | 67.76% |
| extract-the-knowledge-of-graph-neural | 77.3% |
| graph-attention-networks | 41.4% |
| break-the-ceiling-stronger-multi-scale-deep | 71.36% |
| convolutional-neural-networks-on-graphs-with | 33.9% |
| convolutional-networks-on-graphs-for-learning | 50.5% |
| mutual-teaching-for-graph-convolutional | 66.9% |
| break-the-ceiling-stronger-multi-scale-deep | 69.99% |
| gated-graph-sequence-neural-networks | 48.2% |
| diffusion-convolutional-neural-networks | 59.0% |
| lanczosnet-multi-scale-deep-graph | 58.1 ± 8.2 |
| lanczosnet-multi-scale-deep-graph | 60.8 ± 9.0 |
| break-the-ceiling-stronger-multi-scale-deep | 74.89% |
| view-consistent-heterogeneous-network-on | 74.9% |