Compară metode

	Analiza centralității	Rețea Neuronală pe Grafuri	Analiza rețelelor multistrat
Domeniu	Analiza rețelelor	Analiza rețelelor	Analiza rețelelor
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Anul apariției	1979	2017–2018 (major variants)	2013–2014 (formal mathematical framework)
Autorul original	Linton C. Freeman	—	Kivelä et al. (2014); De Domenico et al. (2013)
Tip	Descriptive / exploratory network measure family	Deep learning on graph-structured data	Graph-theoretic network model
Sursa seminală	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.	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.
Denumiri alternative	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	GNN, GCN, GAT, GraphSAGE	multiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks)
Înrudite	5	5	6
Rezumat	Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors.	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.