Methoden vergleichen

	Multilayer Community Detection	Modularitätsanalyse
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	2010–2014	2004
Urheber	Mucha, P. J. et al.; Kivela, M. et al.	Newman, M. E. J. & Girvan, M.
Typ	Community detection algorithm for multilayer networks	Community detection / graph partitioning
Wegweisende Quelle	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.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Aliasnamen	multilayer clustering, multiplex community detection, cross-layer community detection, MCD	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Verwandt	5	5
Zusammenfassung	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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.