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الرئيسية
SOTA
تصنيف العقد
Node Classification On Cora With Public Split
Node Classification On Cora With Public Split
المقاييس
Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
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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