Methoden vergleichen

	Multilayer-Grad-Zentralität	Multilayer-Nähe-Zentralität
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	2013–2014	2013–2014
Urheber	Kivelä, M.; De Domenico, M. et al.	Kivela, M. et al.; De Domenico, M. et al.
Typ	Centrality measure for multilayer networks	Centrality measure for multilayer networks
Wegweisende Quelle	Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.	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.
Aliasnamen	multilayer degree, multiplex degree centrality, overlapping-layer degree centrality, MDC	multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality
Verwandt	6	5
Zusammenfassung	Multilayer degree centrality extends the classic degree centrality measure to networks composed of multiple layers — such as networks representing different types of social ties, communication channels, or relationship contexts simultaneously. It quantifies how many connections a node has across one or all layers, revealing nodes that are influential not just in a single context but across the entire multi-relational structure.	Multilayer closeness centrality extends the classical closeness centrality measure to networks that contain multiple types of relationships or interaction contexts (layers). Rather than treating each layer in isolation, it computes how quickly a node can reach all others by traversing any combination of available layers, revealing nodes that are structurally efficient connectors across the full network system.