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Node Classification On Citeseer 1

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
GCN-FP54.3%Convolutional Networks on Graphs for Learning Molecular Fingerprints-
GGNN56.0%Gated Graph Sequence Neural Networks-
Truncated Krylov69.03%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
AdaLanczosNet63.3 ± 1.8LanczosNet: Multi-Scale Deep Graph Convolutional Networks-
Snowball (tanh)64.23%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
GAT46.5%Graph Attention Networks-
Snowball (linear + tanh)67.07%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
DCNN62.2%Diffusion-Convolutional Neural Networks-
LanczosNet61.3 ± 3.9LanczosNet: Multi-Scale Deep Graph Convolutional Networks-
Snowball (linear)65.85%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
MT-GCN68.9%Mutual Teaching for Graph Convolutional Networks-
GraphSAGE51.0%Inductive Representation Learning on Large Graphs-
VCHN70.1%View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes
ChebyNet59.4%Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering-
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Node Classification On Citeseer 1 | SOTA | HyperAI