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홈
SOTA
Node Classification
Node Classification On Pubmed With Public
Node Classification On Pubmed With Public
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Accuracy
Paper Title
Repository
DAGNN (Ours)
80.5 ± 0.5
Towards Deeper Graph Neural Networks
GGNN
75.8%
Gated Graph Sequence Neural Networks
ChebyNet
69.8%
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
SuperGAT MX
81.7%
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
-
LinkDistMLP
72.41%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
G-APPNP
80.95%
Pre-train and Learn: Preserve Global Information for Graph Neural Networks
Truncated Krylov
81.7%
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
GraphSAGE
76.8%
Inductive Representation Learning on Large Graphs
SSP
80.06 ± 0.34%
Optimization of Graph Neural Networks with Natural Gradient Descent
GCN
81.12 ± 0.52
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
AIR-GCN
80%
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
OGC
83.4%
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
LanczosNet
78.3 ± 0.3
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
GCN(predicted-targets)
80.42%
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
Snowball (linear)
79.10%
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
GGCM
80.8%
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
SSGC
80.4
Simple Spectral Graph Convolution
Graph-MLP
79.91
Graph Entropy Minimization for Semi-supervised Node Classification
GCN+DropEdge
79.60%
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-
CoLinkDistMLP
75.41%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
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