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Node Classification On Cora 1

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname
Accuracy
Paper TitleRepository
GCN-FP59.6%Convolutional Networks on Graphs for Learning Molecular Fingerprints-
GGNN60.5%Gated Graph Sequence Neural Networks-
MT-GCN73.1%Mutual Teaching for Graph Convolutional Networks-
Snowball (tanh)74.78%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
Snowball (linear)73.10%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
Snowball (linear + tanh)74.79%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
AdaLanczosNet67.5 ± 8.7LanczosNet: Multi-Scale Deep Graph Convolutional Networks-
LanczosNet66.1 ± 8.2LanczosNet: Multi-Scale Deep Graph Convolutional Networks-
CPF-ind-APPNP80.24%Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework-
VHCN78.1%View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes
ChebyNet44.2%Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering-
Truncated Krylov78.15%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
GAT48.6%Graph Attention Networks-
GraphSAGE49.0%Inductive Representation Learning on Large Graphs-
DCNN66.4%Diffusion-Convolutional Neural Networks-
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Node Classification On Cora 1 | SOTA | HyperAI