Σύγκριση μεθόδων

	Πολυεπίπεδη Κεντρικότητα Παρεμβολής	Ανίχνευση Κοινοτήτων Πολλαπλών Επιπέδων
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2013–2014	2010–2014
Δημιουργός	De Domenico, M.; Kivelä, M.; Arenas, A. et al.	Mucha, P. J. et al.; Kivela, M. et al.
Τύπος	Centrality measure (multilayer extension)	Community detection algorithm for multilayer networks
Θεμελιώδης πηγή	De 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.	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.
Εναλλακτικές ονομασίες	MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality	multilayer clustering, multiplex community detection, cross-layer community detection, MCD
Συναφείς	5	5
Σύνοψη	Multilayer 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.