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SOTA
노드 분류
Node Classification On Cora
Node Classification On Cora
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Accuracy
Paper Title
SSP
90.16% ± 0.59%
Optimization of Graph Neural Networks with Natural Gradient Descent
SplineCNN
89.48% ± 0.31%
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
ACMII-Snowball-3
89.36% ± 1.26%
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
ACMII-GCN
88.95% ± 1.04%
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
ACM-Snowball-2
88.83% ± 1.49%
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
UGT
88.74±0.6%
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
GAT + SWA
88.66 ± 1.38%
The Split Matters: Flat Minima Methods for Improving the Performance of GNNs
ACM-GCN
88.62% ± 1.22%
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
GCN-LPA
88.5% ± 1.5%
Unifying Graph Convolutional Neural Networks and Label Propagation
LinkDist
88.24%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
CNMPGNN
88.20±1.22%
CN-Motifs Perceptive Graph Neural Networks
CoLinkDist
87.89%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
3ference
87.78%
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
LinkDistMLP
87.58%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
CoLinkDistMLP
87.54%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
AS-GCN
87.44% ± 0.0034%
Adaptive Sampling Towards Fast Graph Representation Learning
CGT
87.10±1.53
Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
Cleora
86.80%
Cleora: A Simple, Strong and Scalable Graph Embedding Scheme
NodeNet
86.80%
NodeNet: A Graph Regularised Neural Network for Node Classification
DFNet-ATT
86% ± 0.4%
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
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Node Classification On Cora | SOTA | HyperAI초신경