Bandingkan metode

	Deteksi Komunitas Dinamis	Deteksi Komunitas Multilapis
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2010 (key formalization); earlier work 2002–2009	2010–2014
Pencetus	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)	Mucha, P. J. et al.; Kivela, M. et al.
Tipe	Graph clustering / community discovery	Community detection algorithm for multilayer networks
Sumber perintis	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.
Alias	DCD, temporal community detection, evolving community detection, dynamic graph clustering	multilayer clustering, multiplex community detection, cross-layer community detection, MCD
Terkait	5	5
Ringkasan	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.