Salīdzināt metodes

	Grafu neironu tīkls	Daudzslāņu tīklu analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2017–2018 (major variants)	2013–2014 (formal mathematical framework)
Autors	—	Kivelä et al. (2014); De Domenico et al. (2013)
Tips	Deep learning on graph-structured data	Graph-theoretic network model
Pirmavots	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Kivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271.
Citi nosaukumi	GNN, GCN, GAT, GraphSAGE	multiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks)
Saistītās	5	6
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.	Multilayer network analysis is a graph-theoretic framework, formalised by Kivelä et al. (2014) and De Domenico et al. (2013), that represents the same set of nodes simultaneously across multiple relationship layers. Where a single-layer network collapses all relationships into one graph, the multilayer model preserves the distinct relational context of each layer — social platform, biological interaction type, or infrastructure tier — while also modelling how layers couple with each other through interlayer edges.