Bandingkan metode

	Deteksi Komunitas Dinamis	Deteksi Komunitas Temporal
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2010 (key formalization); earlier work 2002–2009	2010
Pencetus	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)	Mucha, P. J. et al.
Tipe	Graph clustering / community discovery	Network clustering algorithm
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.	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.
Alias	DCD, temporal community detection, evolving community detection, dynamic graph clustering	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Terkait	5	6
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.	Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.