Salīdzināt metodes

	Iezīmju izplatīšana	Grafu neironu tīkls
Nozare	Mašīnmācīšanās	Tīklu analīze
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2002	2017–2018 (major variants)
Autors	Zhu, X. & Ghahramani, Z.	—
Tips	Graph-based semi-supervised classification	Deep learning on graph-structured data
Pirmavots	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).
Citi nosaukumi	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	GNN, GCN, GAT, GraphSAGE
Saistītās	3	5
Kopsavilkums	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.	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.