Node Classification On Cora With Public Split

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称	Accuracy	Paper Title	Repository
CoLinkDistMLP	81.19%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
AdaLanczosNet	80.4 ± 1.1	LanczosNet: Multi-Scale Deep Graph Convolutional Networks
GCN-TV	86.3%	Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
ChebyNet	78.0%	Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
GRAND	85.4 ± 0.4	Graph Random Neural Network for Semi-Supervised Learning on Graphs
GGNN	77.6%	Gated Graph Sequence Neural Networks
GAT	83.0 ± 0.7%	Graph Attention Networks
OGC	86.9%	From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Snowball (linear)	83.26%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
GEM	83.05%	Graph Entropy Minimization for Semi-supervised Node Classification
CPF-ind-APPNP	85.3%	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
G-APPNP	84.31%	Pre-train and Learn: Preserve Global Information for Graph Neural Networks
GAT+PGN	83.26 ± 0.69%	The Split Matters: Flat Minima Methods for Improving the Performance of GNNs
LinkDistMLP	80.79%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
DCNN	79.7%	Diffusion-Convolutional Neural Networks
AIR-GCN	84.7%	GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
SuperGAT MX	84.3%	How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision	-
GCN	85.1 ± 0.7	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GGCM	83.6%	From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
CoLinkDist	81.39%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages

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