Salīdzināt metodes

	Grafu neironu tīkls	Spektrālā klasterizācija
Nozare	Tīklu analīze	Mašīnmācīšanās
Saime	Process / pipeline	Machine learning
Izcelsmes gads	2017–2018 (major variants)	2002
Autors	—	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Tips	Deep learning on graph-structured data	Graph-based clustering (spectral method)
Pirmavots	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856.
Citi nosaukumi	GNN, GCN, GAT, GraphSAGE	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Saistītās	5	5
Kopsavilkums	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.	Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate.