Node Classification On Citeseer With Public
المقاييس
Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | Accuracy |
|---|---|
| graphair-graph-representation-learning-with | 72.9% |
| lanczosnet-multi-scale-deep-graph | 66.2 ± 1.9 |
| bridging-the-gap-between-spectral-and-spatial | 73.3 |
| distilling-self-knowledge-from-contrastive | 70.27% |
| graph-random-neural-network | 75.4 ± 0.4 |
| break-the-ceiling-stronger-multi-scale-deep | 73.32% |
| pre-train-and-learn-preserve-global | 72% |
| النموذج 8 | 72.70% |
| extract-the-knowledge-of-graph-neural | 74.6% |
| distilling-self-knowledge-from-contrastive | 70.79% |
| optimization-of-graph-neural-networks-with | 74.28 ± 0.67% |
| learning-discrete-structures-for-graph-neural | 75.0% |
| from-cluster-assumption-to-graph-convolution | 77.5 |
| distilling-self-knowledge-from-contrastive | 70.96% |
| every-node-counts-self-ensembling-graph | 73.4 ± 0.7 |
| graph-attention-networks | 72.5 ± 0.7% |
| how-to-find-your-friendly-neighborhood-graph-1 | 72.6% |
| graphmix-regularized-training-of-graph-neural | 74.52 ± 0.59 |
| inductive-representation-learning-on-large | 67.2 |
| simple-spectral-graph-convolution | 73.6 |
| break-the-ceiling-stronger-multi-scale-deep | 72.85% |
| convolutional-neural-networks-on-graphs-with | 70.1% |
| simple-and-deep-graph-convolutional-networks-1 | 73.4% |
| semi-supervised-node-classification-via | 72.8% |
| graph-entropy-minimization-for-semi | 72.63 |
| graph-entropy-minimization-for-semi | 74.2 |
| convolutional-networks-on-graphs-for-learning | 61.5% |
| a-flexible-generative-framework-for-graph | 74.5% |
| classic-gnns-are-strong-baselines-reassessing | 73.14± 0.67 |
| break-the-ceiling-stronger-multi-scale-deep | 73.86% |
| from-cluster-assumption-to-graph-convolution | 74.2 |
| diffusion-convolutional-neural-networks | 69.4% |
| neural-message-passing-for-quantum-chemistry | 64.0 |
| graph-entropy-minimization-for-semi | 73.53 |
| data-augmentation-for-graph-neural-networks | 73.3 ± 1.1 |
| towards-deeper-graph-neural-networks | 73.3 ± 0.6 |
| gated-graph-sequence-neural-networks | 64.6% |
| lanczosnet-multi-scale-deep-graph | 68.7 ± 1.0 |
| distilling-self-knowledge-from-contrastive | 70.26% |
| the-split-matters-flat-minima-methods-for | 74.73 ± 0.6% |