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Mnohovrstevná středovost podle průchodnosti×Detekce komunit ve vícevrstvých sítích×
OborAnalýza sítíAnalýza sítí
RodinaMachine learningMachine learning
Rok vzniku2013–20142010–2014
TvůrceDe Domenico, M.; Kivelä, M.; Arenas, A. et al.Mucha, P. J. et al.; Kivela, M. et al.
TypCentrality measure (multilayer extension)Community detection algorithm for multilayer networks
Původní zdrojDe Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Gómez, S., & Arenas, A. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022. DOI ↗Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗
Další názvyMBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centralitymultilayer clustering, multiplex community detection, cross-layer community detection, MCD
Příbuzné55
ShrnutíMultilayer betweenness centrality extends the classical betweenness measure to networks with multiple types of relationships — or layers — by computing how often a node lies on shortest paths that can traverse any layer or switch between layers. It identifies brokers and bridges whose influence spans distinct interaction domains simultaneously.Multilayer community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss.
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ScholarGatePorovnat metody: Multilayer Betweenness Centrality · Multilayer Community Detection. Získáno 2026-06-17 z https://scholargate.app/cs/compare