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Node Classification On Citeseer With Public

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Paper Title
OGC77.5From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
GRAND75.4 ± 0.4Graph Random Neural Network for Semi-Supervised Learning on Graphs
LDS-GNN75.0%Learning Discrete Structures for Graph Neural Networks
Graph-MLP + PGN74.73 ± 0.6%The Split Matters: Flat Minima Methods for Improving the Performance of GNNs
CPF-tra-APPNP74.6%Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
GraphMix(GCN)74.52 ± 0.59GraphMix: Improved Training of GNNs for Semi-Supervised Learning
G3NN74.5%A Flexible Generative Framework for Graph-based Semi-supervised Learning
SSP74.28 ± 0.67%Optimization of Graph Neural Networks with Natural Gradient Descent
GEM74.2Graph Entropy Minimization for Semi-supervised Node Classification
GGCM74.2From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Truncated Krylov73.86%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
SSGC73.6Simple Spectral Graph Convolution
OKDEEM73.53Graph Entropy Minimization for Semi-supervised Node Classification
SEGCN73.4 ± 0.7Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
GCNII73.4%Simple and Deep Graph Convolutional Networks
Snowball (tanh)73.32%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
DSGCN73.3Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
GCN+GAugO73.3 ± 1.1Data Augmentation for Graph Neural Networks
DAGNN (Ours)73.3 ± 0.6Towards Deeper Graph Neural Networks
GCN73.14± 0.67Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
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Node Classification On Citeseer With Public | SOTA | HyperAI