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Node Classification On Cora 1

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title	Repository
CPF-ind-APPNP	80.24%	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
Truncated Krylov	78.15%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
VHCN	78.1%	View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes	-
Snowball (linear + tanh)	74.79%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (tanh)	74.78%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
MT-GCN	73.1%	Mutual Teaching for Graph Convolutional Networks
Snowball (linear)	73.10%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
AdaLanczosNet	67.5 ± 8.7	LanczosNet: Multi-Scale Deep Graph Convolutional Networks
DCNN	66.4%	Diffusion-Convolutional Neural Networks
LanczosNet	66.1 ± 8.2	LanczosNet: Multi-Scale Deep Graph Convolutional Networks
GGNN	60.5%	Gated Graph Sequence Neural Networks
GCN-FP	59.6%	Convolutional Networks on Graphs for Learning Molecular Fingerprints
GraphSAGE	49.0%	Inductive Representation Learning on Large Graphs
GAT	48.6%	Graph Attention Networks
ChebyNet	44.2%	Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

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Node Classification On Cora 1 | SOTA | HyperAI