Node Classification On Pubmed 003

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
VCHN	71.8%	View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes
Truncated Krylov	71.11%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (linear)	68.12%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
MT-GCN	65.5%	Mutual Teaching for Graph Convolutional Networks
Snowball (tanh)	62.61%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (linear + tanh)	61.94%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
AdaLanczosNet	61%	LanczosNet: Multi-Scale Deep Graph Convolutional Networks
DCNN	60.9%	Diffusion-Convolutional Neural Networks
LanczosNet	60.4 ± 8.6	LanczosNet: Multi-Scale Deep Graph Convolutional Networks
GCN-FP	56.2%	Convolutional Networks on Graphs for Learning Molecular Fingerprints
GGNN	55.8%	Gated Graph Sequence Neural Networks
GAT	50.9%	Graph Attention Networks
GraphSAGE	45.4%	Inductive Representation Learning on Large Graphs
ChebyNet	45.3%	Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

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Node Classification On Pubmed 003 | SOTA | HyperAI