Comparer des méthodes

	Réseau neuronal graphique semi-supervisé	Apprentissage semi-supervisé
Domaine	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017 (GCN formulation); 2004 (label propagation roots)	1970s–2006 (formalized)
Auteur d'origine	Kipf, T. N. & Welling, M. (canonical formulation); Zhou et al. (label propagation precursor)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Semi-supervised graph representation learning	Learning paradigm
Source fondatrice	Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR 2017).	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	Semi-supervised GNN, GNN semi-supervised learning, graph-based semi-supervised classification, semi-supervised node classification	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	4	5
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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.