Node Classification On Cora 05

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
CPF-ind_APPNP	77.3%	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
VCHN	74.9%	View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes
Truncated Krylov	74.89%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (tanh)	71.36%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (linear)	69.99%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball (linear + tanh)	67.76%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
MT-GCN	66.9%	Mutual Teaching for Graph Convolutional Networks
AdaLanczosNet	60.8 ± 9.0	LanczosNet: Multi-Scale Deep Graph Convolutional Networks
DCNN	59.0%	Diffusion-Convolutional Neural Networks
LanczosNet	58.1 ± 8.2	LanczosNet: Multi-Scale Deep Graph Convolutional Networks
GCN-FP	50.5%	Convolutional Networks on Graphs for Learning Molecular Fingerprints
GGNN	48.2%	Gated Graph Sequence Neural Networks
GAT	41.4%	Graph Attention Networks
GraphSAGE	37.5%	Inductive Representation Learning on Large Graphs
ChebyNet	33.9%	Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

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