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SOTA
Node Classification
Node Classification On Citeseer 1
Node Classification On Citeseer 1
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
VCHN
70.1%
View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes
Truncated Krylov
69.03%
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
MT-GCN
68.9%
Mutual Teaching for Graph Convolutional Networks
Snowball (linear + tanh)
67.07%
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (linear)
65.85%
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (tanh)
64.23%
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
AdaLanczosNet
63.3 ± 1.8
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
DCNN
62.2%
Diffusion-Convolutional Neural Networks
LanczosNet
61.3 ± 3.9
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
ChebyNet
59.4%
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
GGNN
56.0%
Gated Graph Sequence Neural Networks
GCN-FP
54.3%
Convolutional Networks on Graphs for Learning Molecular Fingerprints
GraphSAGE
51.0%
Inductive Representation Learning on Large Graphs
GAT
46.5%
Graph Attention Networks
0 of 14 row(s) selected.
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Node Classification On Citeseer 1 | SOTA | HyperAI