Comparer des méthodes

	Réseau neuronal graphique semi-supervisé	Réseau de neurones convolutifs sur graphes (GCN)
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2017 (GCN formulation); 2004 (label propagation roots)	2017
Auteur d'origine	Kipf, T. N. & Welling, M. (canonical formulation); Zhou et al. (label propagation precursor)	Kipf, T. N. & Welling, M.
Type	Semi-supervised graph representation learning	Spectral graph neural network (semi-supervised node classification)
Source fondatrice	Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR 2017).	Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), Toulon, France.
Alias	Semi-supervised GNN, GNN semi-supervised learning, graph-based semi-supervised classification, semi-supervised node classification	GCN, graph convolutional network, spectral graph convolution, Kipf-Welling GCN
Apparentées	4	1
Résumé	A semi-supervised graph neural network trains a GNN on a graph where only a small fraction of nodes carry labels, using neighborhood message-passing to spread information from labeled nodes to unlabeled ones. The approach, popularised by Kipf and Welling's 2017 Graph Convolutional Network, achieves strong node-classification accuracy even when labeled examples are scarce.	Graph Convolutional Network (GCN) is a foundational deep learning architecture for graph-structured data, introduced by Thomas N. Kipf and Max Welling at ICLR 2017. It extends the convolution operation to irregular graph domains via a first-order spectral approximation, enabling each node to aggregate feature information from its neighbors. The model became the canonical baseline for semi-supervised node classification and sparked the modern graph neural network research agenda.