Σύγκριση μεθόδων

	Δυναμική Ανίχνευση Κοινοτήτων	Ανίχνευση Κοινοτήτων Πολλαπλών Επιπέδων
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2010 (key formalization); earlier work 2002–2009	2010–2014
Δημιουργός	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)	Mucha, P. J. et al.; Kivela, M. et al.
Τύπος	Graph clustering / community discovery	Community detection algorithm for multilayer networks
Θεμελιώδης πηγή	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.	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	DCD, temporal community detection, evolving community detection, dynamic graph clustering	multilayer clustering, multiplex community detection, cross-layer community detection, MCD
Συναφείς	5	5
Σύνοψη	Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.	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.