Comparer des méthodes

	Réseau neuronal à graphes	Spectral Clustering
Domaine	Analyse de réseaux	Apprentissage automatique
Famille	Process / pipeline	Machine learning
Année d'origine	2017–2018 (major variants)	2002
Auteur d'origine	—	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Type	Deep learning on graph-structured data	Graph-based clustering (spectral method)
Source fondatrice	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.
Alias	GNN, GCN, GAT, GraphSAGE	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Apparentées	5	5
Résumé	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.