Node Classification On Cora 3

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
CPF-tra-GCNII	84.18%	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
VCHN	83.1%	View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes
Truncated Krylov	81.92%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (linear)	80.96%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (tanh)	80.72%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (linear + tanh)	79.52%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
MT-GCN	78.5%	Mutual Teaching for Graph Convolutional Networks
AdaLanczosNet	77.7 ± 2.4	LanczosNet: Multi-Scale Deep Graph Convolutional Networks
DCNN	76.7%	Diffusion-Convolutional Neural Networks
LanczosNet	76.3 ± 2.3	LanczosNet: Multi-Scale Deep Graph Convolutional Networks
GGNN	73.1%	Gated Graph Sequence Neural Networks
GCN-FP	71.7%	Convolutional Networks on Graphs for Learning Molecular Fingerprints
GraphSAGE	64.2%	Inductive Representation Learning on Large Graphs
ChebyNet	62.1%	Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
GAT	56.8%	Graph Attention Networks

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Node Classification On Cora 3 | SOTA | HyperAI