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Node Classification On Cora With Public Split

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Paper Title
OGC86.9%From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
GCN-TV86.3%Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
GCNII85.5%Simple and Deep Graph Convolutional Networks
GRAND85.4 ± 0.4Graph Random Neural Network for Semi-Supervised Learning on Graphs
CPF-ind-APPNP85.3%Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
GCN85.1 ± 0.7Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
AIR-GCN84.7%GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
H-GCN84.5%Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
DAGNN (Ours)84.4 ± 0.5Towards Deeper Graph Neural Networks
G-APPNP84.31%Pre-train and Learn: Preserve Global Information for Graph Neural Networks
SuperGAT MX84.3%How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
DSGCN84.2%Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
LDS-GNN84.1%Learning Discrete Structures for Graph Neural Networks
GraphMix83.94 ± 0.57GraphMix: Improved Training of GNNs for Semi-Supervised Learning
GraphMix (GCN)83.94 ± 0.57GraphMix: Improved Training of GNNs for Semi-Supervised Learning
GGCM83.6%From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
GCN+GAugO83.6 ± 0.5%Data Augmentation for Graph Neural Networks
Snowball (linear)83.26%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
GAT+PGN83.26 ± 0.69%The Split Matters: Flat Minima Methods for Improving the Performance of GNNs
Snowball (tanh)83.19%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
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Node Classification On Cora With Public Split | SOTA | HyperAI