手法を比較

	多層媒介中心性 (Multilayer Betweenness Centrality)	多層コミュニティ検出
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Machine learning
提唱年	2013–2014	2010–2014
提唱者	De Domenico, M.; Kivelä, M.; Arenas, A. et al.	Mucha, P. J. et al.; Kivela, M. et al.
種類	Centrality measure (multilayer extension)	Community detection algorithm for multilayer networks
原典	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.
別名	MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality	multilayer clustering, multiplex community detection, cross-layer community detection, MCD
関連	5	5
概要	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.