HyperAI
HyperAI
Startseite
Plattform
Dokumentation
Neuigkeiten
Forschungsarbeiten
Tutorials
Datensätze
Wiki
SOTA
LLM-Modelle
GPU-Rangliste
Veranstaltungen
Suche
Über
Nutzungsbedingungen
Datenschutzrichtlinie
Deutsch
HyperAI
HyperAI
Toggle Sidebar
Seite durchsuchen…
⌘
K
Command Palette
Search for a command to run...
Plattform
Startseite
SOTA
Knotenklassifikation
Node Classification On Cora With Public Split
Node Classification On Cora With Public Split
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
Paper Title
OGC
86.9%
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
GCN-TV
86.3%
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
GCNII
85.5%
Simple and Deep Graph Convolutional Networks
GRAND
85.4 ± 0.4
Graph Random Neural Network for Semi-Supervised Learning on Graphs
CPF-ind-APPNP
85.3%
Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
GCN
85.1 ± 0.7
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
AIR-GCN
84.7%
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
H-GCN
84.5%
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
DAGNN (Ours)
84.4 ± 0.5
Towards Deeper Graph Neural Networks
G-APPNP
84.31%
Pre-train and Learn: Preserve Global Information for Graph Neural Networks
SuperGAT MX
84.3%
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
DSGCN
84.2%
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
LDS-GNN
84.1%
Learning Discrete Structures for Graph Neural Networks
GraphMix
83.94 ± 0.57
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
GraphMix (GCN)
83.94 ± 0.57
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
GGCM
83.6%
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
GCN+GAugO
83.6 ± 0.5%
Data Augmentation for Graph Neural Networks
Snowball (linear)
83.26%
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
GAT+PGN
83.26 ± 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
0 of 36 row(s) selected.
Previous
Next
Node Classification On Cora With Public Split | SOTA | HyperAI