Node Classification On Citeseer 1

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		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.

Node Classification On Citeseer 1 | SOTA | HyperAI