Salīdzināt metodes

	Daļēji uzraudzīts grafu neironu tīkls	Grafu neironu tīkls
Nozare	Dziļā mācīšanās	Tīklu analīze
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2017 (GCN formulation); 2004 (label propagation roots)	2017–2018 (major variants)
Autors	Kipf, T. N. & Welling, M. (canonical formulation); Zhou et al. (label propagation precursor)	—
Tips	Semi-supervised graph representation learning	Deep learning on graph-structured data
Pirmavots	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. International Conference on Learning Representations (ICLR).
Citi nosaukumi	Semi-supervised GNN, GNN semi-supervised learning, graph-based semi-supervised classification, semi-supervised node classification	GNN, GCN, GAT, GraphSAGE
Saistītās	4	5
Kopsavilkums	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.	A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes.