Node Classification On Cora Full Supervised
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Accuracy |
|---|---|
| fdgatii-fast-dynamic-graph-attention-with | 87.7867% |
| fastgcn-fast-learning-with-graph | 85.00% |
| simple-and-deep-graph-convolutional-networks-1 | 88.49% |
| adaptive-sampling-towards-fast-graph | 87.44±0.0034% |
| clarify-confused-nodes-through-separated | 73.42 ± 0.58% |
| graphmix-regularized-training-of-graph-neural | 61.8% |
| beyond-homophily-with-graph-echo-state-1 | 86.0±1.0 |
| inductive-representation-learning-on-large | 82.2% |
| the-truly-deep-graph-convolutional-networks | 88.2% |