Σύγκριση μεθόδων

	Ανίχνευση Κοινοτήτων Πολλαπλών Επιπέδων	Ανάλυση Συνεκτικότητας
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2010–2014	2004
Δημιουργός	Mucha, P. J. et al.; Kivela, M. et al.	Newman, M. E. J. & Girvan, M.
Τύπος	Community detection algorithm for multilayer networks	Community detection / graph partitioning
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	multilayer clustering, multiplex community detection, cross-layer community detection, MCD	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Συναφείς	5	5
Σύνοψη	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.